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SCHOOL OF PUBLIC HEALTH
COLLEGE OF HEALTH SCIENCES
UNIVERSITY OF GHANA
HYPERTENSION AND ASSOCIATED FACTORS IN PATIENTS
ATTENDING HIV CLINIC AT THE KORLE-BU TEACHING
HOSPITAL IN ACCRA
BY
EDMUND TETTEH NARTEY
(10096903)
THIS THESIS IS SUBMITTED TO THE UNIVERSITY OF
GHANA, LEGON IN PARTIAL FULFILMENT OF THE
REQUIREMENT FOR THE AWARD OF
PhD PUBLIC HEALTH DEGREE
JULY, 2017
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DECLARATION
I, Edmund Tetteh Nartey, declare that except for other people's investigations which
have been duly acknowledged, this work is the result of my own original research
carried out under the supervision of Professor Richard Adanu, Dr Bismark Sarfo and
Dr Francis Anto. and that this thesis, either in whole or in part has not been presented
elsewhere for another degree.
.J~(~
EDMUND TETTEH NARTEY (STUDENT) DATE
IPI{/ZO/f
............. / .
PROFESSOR RICHARD ADANU (SUPERVISOR) DATE
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DATE
DATE
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DEDICATION
This thesis is dedicated to the memory of my late father,
Ex-Cpl John Kingsley Kwame Nartey (1924-2013)
III
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ACKNOWLEDGEMENT
My foremost and profound gratitude goes to Jehovah God for his faithfulness to me in
undertaking and completing this work.
I am deeply indebted to my supervisors, Prof. Richard Adanu, Dr. Bismark Sarfo and
Dr Francis Anto, all of the School of Public Health, University of Ghana for their
guidance, remarks and excellent suggestions throughout this successful academic
journey. I am privileged to be mentored by these great academic advisors.
I wish to also acknowledge the Office of Research, Innovation and Development
(ORID) of the University of Ghana for the financial support they offered me to
undertake this work. To Prof. Andrew Adjei and his team, I say, a big thank you.
A special thank you also goes to my senior colleague and friend, Mr Raymond Tetteh
of the Pharmacy Department of the Korle-Bu Teaching Hospital and to Prof. Margaret
Lartey of SMD, UG for their continuous interest to see me complete this programme.
I am also grateful to my wife, Mrs Charlotte Naa Anowah Sackey-Nartey and my two
children, Michael and Nathaniel for their support, understanding and sacrifices offered
during the course of this work.
I am also grateful to the entire staff and patients of Fevers Unit, Korle-Bu Teaching
Hospital for the assistance they gave me during the data collection period.
I also thank the staff of Centre for Tropical Clinical Pharmacology and Therapeutics
(SMD, UG) for their support and wards of encouragement throughout this work.
To all my colleague students (Cohort 1) of School of Public Health, University of
Ghana, I say a big thank you for the words of encouragement throughout this episode
of academic achievement.
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ABSTRACT
Two decades into the highly active antiretroviral therapy (ART) era, there has been a
lot of improvement in the management of HIV/AIDS. Persons Living with HIV
(PLHIV) now have life expectancy near that of the general population. However, with
increasing years of survival, patients will transit into age groups with greater prevalence
of cardiovascular events including hypertension. PLHIV in sub-Saharan Africa are in
addition faced with an epidemiological transition of an increasing burden of non-
communicable diseases. With the debate still ongoing, on whether ART is associated
with hypertension, this thesis aimed to determine the prevalence of hypertension and
associated factors among patients attending the HIV clinic at the Korle-Bu Teaching
Hospital (KBTH) and also estimates the average treatment effect on the treated (ATT)
of ART on hypertension and blood pressure values.
The study design was cross sectional and 311 PLHIV were randomly selected into the
study. The WHO STEPwise Approach to Chronic Disease Risk Factor Surveillance
instrument was modified and used to collect data on socio-demographic, life-style
factors, anthropometric, biochemical and HIV/ART-related parameters from study
participants' clinical folders. The prevalence of hypertension was 36.7% (95% CI, 31.3-
42.3). Study participants on ART had a significantly higher prevalence of hypertension
(41.3% [95% CI, 35.2-47.3]) compared with their ART-naive counterparts (16.9%,
[95% CI, 7.4-26.5]). Regression modelling indicated the factors associated with
hypertension were, increasing age, positive family history of cardiovascular
disease/hypertension, inadequate exercising, a BMI ::::25.0kg/rn", abdominal obesity,
hypercholesterolemia and exposure to ART. Propensity score-matching analysis
estimated an ATT of ART on systolic and diastolic blood pressure values of 12.0 mmHg
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(95% CI, 5.7-18.3) and 6.1mmHg (95% CI, 1.3-10.8) respectively and on hypertension
of26.2 % (95% CI, 13.3-39.1). Estimated risk of cardiovascular disease using the Data
Collection on Adverse Events of anti-Hl V Drugs (D:A:D) risk score indicated 52.4%
of the study participants were of moderate to high risk of cardiovascular event.
This study showed that hypertension, as a cardiovascular risk factor is prevalent among
patients on ART attending HIV clinic at the KBTH. It also established a plausible causal
relation between ART and hypertension/blood pressure levels and estimated that most
patients were of moderate to high risk of cardiovascular event.
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TABLE OF CONTENTS
Content Page
DECLARA TION 11
DEDICATION 111
ACKNOWLEDGEMENT IV
ABSTRACT V
TABLE OF CONTENTS Vll
LIST OF TABLES xv
LIST OF FIGURES XV11
LIST OF ABBREVIA nONS AND ACRONYMS XV111
CHAPTER ONE
1.0 INTRODUCTION 1
1.1 Hypertension
1.2 Human Immunodeficiency Virus (HIV)/ Acquired Immune
Deficiency Syndrome (AIDS) 2
1.3 HIV /AIDS and hypertension 4
1.4 Statement of the problem 5
1.5 Conceptual framework 9
1.6 Justification 11
1.7 Objectives 13
1.7.1 General objective 13
1.7.2 Specific objectives 13
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CHAPTER TWO
2.0 LITERA TURE REVIEW 14
2.1 Hypertension 14
2.1.1 Signs and symptoms of hypertension 15
2.1.2 Types of hypertension 15
2.1.2.1 Primary hypertension 15
2.1.2.2 Secondary hypertension 16
2.1.3 Pathogenesis and pathophysiology of essential hypertension 16
2.1.4 Measurement of blood pressure in adults 17
2.1.5 Diagnosis and stages of hypertension in adults 19
2.1.6 Prevention of hypertension 20
2.1.7 Management of hypertension 21
2.1.8 Epidemiology of hypertension worldwide 22
2.1.9 Epidemiology of hypertension in Ghana 26
2.2 Human Immunodeficiency Virus (HIV) and Acquired Immune
Deficiency Syndrome (AIDS) 29
2.2.1 Background to the HIV /AIDS pandemic 30
2.2.2 Global HIV/AIDS epidemiology 31
2.2.3 HIV classification and sub-types 33
2.2.4 HIV transmission and risk factors 34
2.2.4.1 Transmission of HIV through sexual intercourse 34
2.2.4.2 Transmission of HIV through contaminated blood/blood products 34
2.2.4.3 Mother-to-child transmission of HIV 35
2.2.5 HIV life-cycle and pathogenesis 35
2.2.6 Stages of HIV infection 41
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2.2.7 HIV infection classification 41
2.2.8 Therapeutic management of HIV infection 43
2.2.9 HIV/AIDS epidemiology in Ghana 48
2.2.10 Anti-retroviral therapy and HIV management in Ghana 50
2.3 Cardiovascular diseases, hypertension, HIV IAIDS and ART 51
2.3.1 Cardiovascular diseases in PLHIV 52
2.3.2 Cardiovascular disease in PLHIV in sub-Saharan Africa 54
2.3.3 Hypertension as a cardiovascular risk factor in PLHIV 58
2.3.4 Prevalence of hypertension in PLHIV in sub-Saharan Africa 59
2.3.5 Review of factors associated with hypertension in PLHIV 63
2.3.5.1 Socio-demographic and life-style factors 63
2.3.5.1.1 Age 63
2.3.5.1.2 Sex 64
2.3.5.1.3 Marital status 66
2.3.5.1.4 Education and employment status 66
2.3.5.1.5 Alcohol use 66
2.3.5.1.6 Smoking of tobacco 67
2.3.5.1.7 Regular physical activity 68
2.3.5.1.8 Fruit and vegetable consumption 69
2.3.5.1.9 Family history of cardiovascular disease 69
2.3.5.2 Anthropometric and biochemical/metabolic factors 69
2.3.5.2.1 Body mass index (general obesity) 70
2.3.5.2.2 Waist-to-Hip ratio and waist circumference (abdominal obesity) 71
2.3.5.2.3 Diabetes 72
2.3.5.2.4 Dyslipidaemia 74
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2.3.5.2.5 Renal impairment 75
2.3.5.3 HIV /ART-related factors 76
2.3.5.3.1 HIV, viral load and duration of infection 76
2.3.5.3.2 CD4+ T cell count 78
2.3.5.3.3 Antiretroviral therapy 79
2.3.6 Pathogenesis/pathophysiology of hypertension in PLHIV 83
2.3.6.1 The HIV -infection 85
2.3.6.2 Anti-retrovirals used in ART 85
2.4 Propensity score-matching analysis 86
2.4.1 Steps used in propensity score-matching analysis 89
2.4.1.1 Selection of covariates to include in estimating propensity score 89
2.4.1.2 Balance of estimated propensity score across treatment and
comparison groups 90
2.4.1.3 Balance of covariates across treatment and comparison group
within blocks of the estimated propensity score 90
2.4.1.4 Choosing a matching or weighting strategy 91
2.4.1.5 Assessment of balance of covariates after matching or weighting
the sample using the estimated propensity score 92
2.4.1 Analysis of data matched or weighted by the propensity score 93
2.4.2 Post-estimation sensitivity analysis 93
2.5 Cardiovascular risk scoring systems 94
2.5.1 The Framingham 10-year general cardiovascular disease risk score 95
2.5.2 The D:A:D 5-year cardiovascular disease risk score 96
2.5.3 The WHO/ISH risk prediction charts 97
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CHAPTER THREE
3.0 METHODS 98
3.1 Study design 98
3.2 Study location 99
3.3 Study population 100
3.4 Variables 101
3.5 Sampling 108
3.5.1 Sample size calculation 108
3.5.2 Sampling procedure 108
3.5.2.1 Inclusion criteria 108
3.5.2.2 Exclusion criteria 109
3.5.2.3 Selection of study participants 109
3.6 Data collection techniques 110
3.6.1 Consenting process and participation in study 110
3.6.2 Data collection from study participants 110
3.6.2.1 Socio-demographic and life-style characteristics 111
3.6.2.2 Blood pressure, anthropometric and biochemical measurements 111
3.6.2.2.1 Blood pressure measurements 111
3.6.2.2.2 Weight measurement 112
3.6.2.2.3 Height measurement 112
3.6.2.2.4 Waist circumference measurement 112
3.6.2.2.5 Hip circumference measurement 113
3.6.2.2.6 Sample collection and preparation for biochemical measurements 113
3.6.2.2.7 CD4+ T cell count 113
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3.6.2.2.8 Fasting plasma glucose 114
3.6.2.2.9 Total cholesterol 115
3.6.2.2.10 High-density lipoprotein cholesterol 115
3.6.2.2.11 Triglycerides 116
3.6.2.2.12 Low-density lipoprotein cholesterol 117
3.6.2.2.13 Creatinine 117
3.6.2.3 HIV /ART -related and other data extracted from clinical folders 118
3.6.3 Quality control 118
3.7 Data management and statistical analysis 119
3.7.1 Data management 119
3.7.2 Statistical analysis 119
3.7.2.1 Descriptive analysis to determine prevalence of hypertension 120
3.7.2.2 Logistic regression modelling to determine factors associated
with hypertension 120
3.7.2.3 Propensity score-matching analysis to estimate average treatment
effect of ART on hypertension and blood pressure values 123
3.7.2.4 Analysis to estimate cardiovascular disease risk scores 124
3.8 Ethical consideration 125
3.8.1 Ethical clearance from Institutional Review Board 125
3.8.2 Consent from HIV Clinic 125
3.8.3 Informed Consent from study participants 125
3.8.4 Data safety 125
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CHAPTER FOUR
4.0 RESULTS 126
4.1 Description of study participants 126
4.1.1 Socio-demographic and life-style characteristics of study participants 126
4.1.2 Blood pressure levels, anthropometric and biochemical indices
of study participants 128
4.1.3 HIV /ART related characteristics of study participants 130
4.2 Prevalence of hypertension in study participants 131
4.3 Logistic regression modelling of factors associated with hypertension
in study participants 132
4.3.1 Univariate analysis of factors associated with hypertension 132
4.3.2 Multiple logistic regression analysis of factors associated with
hypertension 137
4.4 Propensity score-matching analysis of ATT of ART on hypertension 140
4.4.1 Characteristics of ART -exposed and ART -naive study participants
in the PSM analysis 140
4.4.2 Estimation of propensity scores for ART-exposed and ART-naive
study participants in propensity score-matching analysis 142
4.4.3 Matching of ART-exposed to ART-naIve study participants using
the estimated propensity scores in the PSM analysis 147
4.4.4 Estimation of ATT of ART exposure on hypertension and blood
pressure values in the PSM analysis 152
4.5 Cardiovascular disease risk score assessment 153
4.6 Summary of results 154
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CHAPTER FIVE
5.0 DISCUSSION 156
5.1 Introduction 156
5.2 General characteristics of study participants 156
5.3 Prevalence of hypertension in patients attending HIV clinic at KBTH 158
5.4 Factors associated with hypertension 160
5.5 Estimated average treatment effect of ART exposure on hypertension
And blood pressure values 171
5.6 Cardiovascular risk score assessment 173
5.7 General discussion 176
5.8 Study limitations 177
5.9 Study strengths and contribution to knowledge 179
CHAPTER SIX
6.0 CONCLUSIONS AND RECOMMENDATIONS 180
6.1 Conclusions 180
6.2 Recommendations 181
REFERENCES 183
APPENDICES 225
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LIST OF TABLES
Table 1. Antiretrovirals and their therapeutic classes 45
Table 2. Combination anti-retroviral medicines 46
Table 3. Drug regimens used in managing HIV infection in Ghana 50
Table 4. Studies assessing prevalence of hypertension in people living with
HIV /AIDS in sub-Saharan Africa 61
Table 5. Studies assessing the association between highly active
anti-retroviral therapy and hypertension in people living with
HIV/AIDS in sub-Saharan Africa 82
Table 6. Socio-demographic variables measured 102
Table 7. Family history of cardiovascular disease and life-style variables
measured 103
Table 8. Blood pressure measurements 104
Table 9. Anthropometric variables measured 105
Table 10. Metabolic/biochemical variables measured 106
Table 11. HIV /ART -related variables measured or extracted from clinical
folder 107
Table 12. Socio-demographic and life-style characteristics of study
participants 127
Table 13. Blood pressure levels, anthropometric and biochemical indices of
study participants 129
Table 14. HIV /ART -related characteristics of study participants 131
Table 15. Prevalence of hypertension in study participants 132
Table 16. Univariate analysis of socio-demographic and life-style factors
associated with hypertension in study participants 134
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Table 17. Univariate analysis of anthropometric and metabolic/biochemical
factors associated with hypertension in study participants 135
Table 18 Univariate analysis ofHIV/ART-related factors associated
with hypertension in study participants 136
Table 19 MUltiple logistic regression analysis of factors associated
with hypertension 138
Table 20. Characteristics of study participants in propensity score-matching
analysis 141
Table 21 Covariates balance indicators in the propensity score-matching
study participants 148
Table 22 Estimated average treatment effect of ART exposure on
hypertension and blood pressure values 153
Table 23. Cardiovascular risk assessment score 153
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LIST OF FIGURES
Figure 1. Conceptual framework of the interaction between life-style
factors, anthropometric factors, metabolic factors and
HIV/ART-related factors in the pathogenesis of hypertension
in persons living with HIV 9
Figure 2. Schematic diagram of the human immunodeficiency virus (HIV) 38
Figure 3. HIV lifecycle and antiretroviral drug targets. 39
Figure 4. Hypothetical model for the pathogenesis of cardiovascular disease in
HIV -infected persons taking antiretroviral 84
Figure 5. Receiver operating characteristics for "calibration" of final multiple
logistic regression model of factors associated with hypertension 139
Figure 6. Box-plot of estimated propensity score among ART-exposed
and ART-naive study participants before matching 144
Figure 7. K-density plot of estimated propensity score among ART -exposed
and ART-naive study participants before matching 145
Figure 8. Histogram of estimated propensity score among ART-exposed
and ART- naive study participants before matching 146
Figure 9a. Distribution of estimated propensity score before matching 149
Figure 9b. Distribution of estimated propensity score after matching 149
Figure 10. Histogram showing standardized percentage bias before and
after propensity score matching 150
Figure 11. Standardised % bias across each covariate before and after matching 151
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LIST OF ABBREVIATIONS AND ACRONYMS
3TC Lamivudine
ABC Abacavir
ABPM Ambulatory blood pressure monitoring
ACE Angiotensin converting enzyme
ADRBI Adrenergic receptor B 1
AIDS Acquired Immune-Deficiency Syndrome
aOR Adjusted odds ratio
ARB Angiotensin II receptor blockers
ARVs Antiretrovirals
ART Antiretroviral Therapy
ATP Adenosine triphosphate
ATT Average treatment on the treated (also ATET)
ATV Atazanavir
AUC Area under the curve
AZT Zidovudine (also ZDV)
CBPM Clinic blood pressure monitoring
CCB Calcium channel blockers
CD4 Cluster of differentiation 4
CD8 Cluster of differentiation 8
CDC Centres for Disease Control and Prevention
C.l Conditional independence
CKD Chronic kidney disease
CRF Circulating recombinant forms
CVD Cardiovascular disease
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d4t Stavudine
D:A:D Data Collection on Adverse Events of anti-HIV Drugs
DALY Disability adjusted life years
dBP Diastolic blood pressure
DNA Deoxyribonucleic acid
EFV Efavirenz
eGFR Estimated glomerular filtration rate
ENaC Epithelial sodium channel
F VII Factor VII
FI Fusion inhibitor
FFA Free fatty acids
FPG Fasting plasma glucose
FRS Framingham Risk Score
FTC Emtricitabine
GDHS Ghana Demographic and Health Survey
GWAS Analysis of Genome-wide association studies
HDL-C High-density lipoprotein cholesterol
HIC High-income countries
HIV Human immunodeficiency virus
hs-CRP High-sensitivity C-reactive protein
HSS HIV sentinel site survey
i.i.d Independence and identically distributed sampling
IL6 Interleukin 6
INSTI Integrase strand transfer inhibitor
ISH International Society of Hypertension
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IQR Interquartile range
JNC 7 Joint National Committee on Prevention, Detection, Evaluation and
Treatment of high blood pressure
KBTH Korle-bu Teaching Hospital
LAV Lymphadenopathy -associated virus
LDL-C Low-density lipoprotein cholesterol
LMIC Low- and middle- income countries
LPS Lipopolysaccharide
MACS Multicentre AIDS Cohort study
MDGs Millennium Development Goals
MI Myocardial infarction
mRNA Messenger RNA
NACP National AIDS Control Programme
NCDs Non-communicable diseases
NICE National Institute for Health and Care Excellence
NNRTI Non-nucleoside reverse transcriptase inhibitor
NO Nitric oxide
NRTI Nucleoside Reverse Transcriptase inhibitor
NtRTI Nucleotide Reverse Transcriptase inhibitor
NVP Nevirapine
OR Odds ratio
PAI-I Plasminogen activator inhibitor type 1
PD-I Programmed death I
PI Protease Inhibitor
PLHIV Persons Living with HIV
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PPARy Peroxisome proliferator-activated receptors
PSM Propensity score-matched
PURE Prospective Urban Rural Epidemiology
RAS Renin angiotensin aldosterone system/Renin angiotensin system
RNA Ribonucleic acid
ROC Receiver Operating Characteristics
ROS Reactive oxygen species
RPV Rilpivirine
RTV Ritonavir
SAGE Study on Global Ageing and Health
SSA sub-Saharan Africa
sBP Systolic blood pressure
SD Standard deviation
TC Total cholesterol
TDF Tenofovir disoproxil fumarate
TG Triglycerides
WC Waist circumference
WHO World Health Organisation
WHR Waist-to-hip ratio
WIHS Women's Interagency HIV Study
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CHAPTER ONE
1.0 INTRODUCTION
:1.1 Hypertension
:Bach time the heart beats, blood is pumped from the heart to parts of the body through the
blood vessels (arteries). The force created by blood pushing against the walls of the blood
vessels is termed blood pressure. Hypertension, defined as systolic blood pressure (sBP) 2:140
rnm Hg and/or diastolic blood pressure (dBP) 2:90mm Hg, is also known as high blood pressure
and it is a condition in which the blood vessels have persistently elevated pressure (WHO,
;ZOl3a) . Hypertension occurs as a result of long periods of elevated pressure of the main
arteries. Hypertension rarely causes symptoms (WHO, 2013a) and uncontrolled hypertension
can lead to several outcomes affecting the heart and other vital organs in the body, hence the
term "silent, invisible killer" (WHO, 20l3a). Hypertension is a known risk factor for
cardiovascular diseases (CVDs) and complications of the condition may result in rupture of
blood vessels, renal impairment, retinal haemorrhage, visual impairment and cognitive
impairment (WHO, 2013a; WHO, 20l3b). According to a policy statement from "The World
Hypertension League", hypertension is estimated to be accountable for at least 9.4 million
deaths and 162 million years of life lost in 2010 and also the cause of 50% of heart disease,
stroke, and heart failure, 18% of deaths overall and more than 40% of deaths in people with
diabetes (Campbell et al., 2014). The World Health Organization (WHO) also reported that
globally, hypertension is estimated to cause 7.5 million deaths which is about 12.8% of the
total of all deaths, accounting for an estimated 57 million disability adjusted life years
(DALYS) or 3.7% of total DAL YS (WHO, 2013b). The prevalence of hypertension is also
increasing steadily in low and middle income countries (LMIC) and this has been attributed to
population growth, ageing and other life-style factors like smoking, physical inactivity,
unhealthy diet, harmful use of alcohol and obesity (Singh et al., 2000; Yusuf et al., 2001;
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Seedat, 2004; WHO, 20 13a). This makes hypertension and its associated factors a major public
health issue especially in SSA but which can be prevented and managed appropriately with
population targeted interventions and screening programmes.
1.2 Human Immunodeficiency Virus (HI V) and Acquired Immune Deficiency
Syndrome (AIDS)
Human immunodeficiency virus (HIV) is the virus which causes the disease acquired immune
deficiency syndrome (AIDS) in humans. The virus acts slowly and it may take several years
for the observable clinical manifestation of the disease state. HIV is a lentivirus belonging to a
group of viruses called retro viruses which use ribonucleic acid (RNA) instead of
deoxyribonucleic acid (DNA) in replicating itself within living cells. The virus after entering
into the body typically infects the human immune system attacking the cluster of differentiation
4 T-helper cells (CD4+ T-cells) and other cells and thereby leading to a decrease in CD4+ T-
cells count. This decrease in CD4+ T-cells is through a number of mechanisms including direct
killing of infected cells by the virus, killing of infected cells by cluster of differentiation 8
(CD8) cytotoxic lymphocytes and cell apoptosis of uninfected bystander cells (Garg et aI.,
2012). The decline in CD4+ T-cells (below a critical level) leads to a failure of the immune
system. The functionality of the immune system depends on the presence of the T-cells; hence,
any impairment in this function will lead to the occurrence of infections and the reduction of
immune surveillance against several diseases. In addition to compromising the immune system,
HIV may also attack organs like the kidneys, heart and brain resulting in metabolic pathologies
like renal failure, cardiomyopathy, dementia and encephalopathy. The effect of the virus on
both the immune system and direct attack on some organs if not controlled leads to AIDS
characterised by the occurrence of opportunistic infections, cancers and other pathologies.
Worth noting is that, in situations where a HIV -positive person does not access treatment, the
estimated survival time after the infection is 11 years (UNAIDS, 2007).
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The management of HIV /AIDS has gone through a series of changes. Three decades into the
HTV epidemic (UNAIDS, 2013), a number of critical advances have been made in care and
treatment of HIV positive patients. Anti-retroviral therapy now reaches nearly half of those
eligible, resulting in dramatic reductions of illness and death (WI-10, 2011b). Persons living
with HIV (PLHIV) are living longer than ever before, and are able to establish families, work
and lead normal lives. Treatment of HIV infection commonly requires a combination of 3 to 4
anti-retroviral drugs (ARVs), termed Highly Active Antiretroviral Therapy (ART) (Josephson,
2010). Presently in the ART era, five classes of ARVs are available for use but with specific
combination groups for a patient depending on several factors including the type of HIV
infection, the presence of resistance strains, availability of drug types per national guidelines,
report of adverse events and history with previous ARVs. The different classes of ARVs act at
different stages of the HIV replication cycle. These ARVs either impede the entry of HIV into
host cells (fusion inhibitors-FIs), act as DNA chain terminators (nucleoside/nucleotide reverse
transcriptase inhibitors-NRTlslNtRTIs), hinder the catalytic activity of reverse transcriptase
(non-nucleoside reverse transcriptase inhibitors-NNRTIs), inhibit the incorporation of HIV
DNA into host cell DNA (integrase strand transfer inhibitors-INS TIs) or inhibit production of
infectious viral particles (protease inhibitors-PIs) (Clavel and Hance, 2004). The chronic nature
of HIV infection requires lifelong ART (Negredo et al., 2006) to continuously suppress HIV
viral replication, thus reducing morbidity and mortality. However, ARTis restricted by
treatment barriers such as complex dosing, drug-drug interactions and toxicities. In addition,
some HIV -positive patients still require concomitant treatment with drugs for opportunistic
infections and medication to treat unrelated medical conditions and/or the metabolic
complications of ARV therapy (Fichtenbaum and Gerber, 2002). These metabolic
complications are also risk factors of other morbid conditions like hypertension, type II
diabetes and dyslipidaemia (Busari et al., 2007; Busari and Busari, 2013). Therefore, the
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virtually limitless number of drug combinations that may be taken by patients undergoing
treatment of HIV infection with co-morbidity markedly increases the risk of drug interactions,
treatment failure, medication non-adherence, poor response rate of patients to ART and adverse
drug reactions. The management of co-morbidities is therefore one of the major challenges
associated with the multi drug regimens used for HIV therapy. Among the many co-morbid
conditions, CVDs are of particular concern due to ARV induced metabolic changes. Available
data suggests that even in SSA, chronic cardiovascular and pulmonary diseases are increasing
in HIV-positive patients (Bloomfield et al., 2014a; Hyle et al., 2017). Hence, the risk of
developing chronic cardiovascular and pulmonary diseases is increasingly being recognized as
a major public health problem in PLHIV (Currier et al., 2003; Friis-Moller et al., 2007; Morris
et al., 2012). Consequently, medical care for this population is focusing more intently on
control and prevention of age- and metabolic-related co-morbidities (Aberg, 2009). Of the
various risk factors associated with CVDs, the role played by hypertension as the major and
leading risk factor for CVDs is unanimously agreed.
1.3 Hypertension and HIV/AIDS
Hypertension is an explanatory risk factor for myocardial infarction, stroke, heart failure, and
kidney disease and has been established as a major risk factor that contributes to CVDs in
adults with HIV-infection (Saves et al., 2003; Malvestutto and Aberg, 2010). The prevalence
of hypertension in PLHIV ranges from 13% to 36% (Bergersen et al., 2003; Gazzaruso et al.,
2003; Jerico et al., 2005; Baekken et al., 2008). Hypertension has also been related to mortality
in HIV-positive patients in Kenya (Bloomfield et al., 2014a). It has also been suggested that
the degree to which high blood pressure is related to mortality in HIV -positive patients in SSA
has not been specifically addressed (Moore et al., 2011). Although a relationship between ART
and other components of the metabolic syndrome, such as dyslipidaemia and hyperglycaemia,
have been established, it is not clear whether HIV or specific ARVs are explanatory risk factors
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for hypertension. Studies have demonstrated that there is an association between HIV infection,
ART and hypertension (Crane et al., 2006; Wilson et al., 2009; De Socio et al., 2010) but there
is no conclusive evidence to this as other studies have indicated otherwise (Bergersen et al.,
2003; Baekken et al., 2008).
1.4 Statement of the problem
The introduction of newer anti-retroviral drugs in the form of ART for the management of HIV
has led to a better health care for PLHIV. As more people in SSA begun taking antiretroviral
treatment, mortality rates have dropped (Mermin et al., 2008; Stover et al., 2008) and life
expectancy ofPLHIV has increased with studies indicating PLHIV on ART in SSA can achieve
a normal life expectancy (Mills et al., 2011). As such, HIV infection is now considered a
chronic disease. There is also evidence to indicate that both HIV infection and ART are risk
factors for the development of non-communicable diseases (NCDs) in resource-limited settings
including the likelihood of developing chronic pathologies like hypertension (Fedele et al.,
2011; Post, 2011; Fabian et al., 2013; Kalra and Agrawal, 2013). In sub-Saharan Africa (SSA),
there is ongoing demographic change with several populations showing an increase in life
expectancy and an ageing population. In addition, there is epidemiologic transition in terms of
disease burden from infectious diseases to non-communicable diseases and this has been
attributed to various factors including rapid urbanization, improved healthcare and economic
changes (CFR, 2014). This has resulted in several SSA populations increasingly demonstrating
HIV as an infectious chronic disease with NeD co-morbidities like hypertension, diabetes and
kidney disease (Remais et al., 2013).
In Ghana, the change in demographics indicate that the proportion of the population> 15 years
decreased from 45% in 1960 to 38% in 2010 while the proportion of the population ~65 years
increased from 3% to 5% over the same period (GSS et al., 2015). In addition, data from the
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Ghana Demographic and Health surveys indicate that over the last five decades, life expectancy
at birth has increased from 38 years to 60 years among males and from 43 years to 63 years
among females (GSS et al., 2015). In view that the prevalence ofNCDs, including hypertension
is also increasing rapidly (Bosu, 2010), this brings with it an interaction between HIV, ARVs
and hypertension in the HIV -infection population, which needs to be investigated especially in
resource limited settings like Ghana. Anecdotal evidence from the KBTH indicates a
prevalence of hypertension of 25% among patients attending HIV clinic which is higher than
the 2014 Ghana Demographic and Health Survey report of hypertension prevalence of 13% in
the general population of 15-49 years (GSS et al., 2015). Given that the patient population with
HIV is increasingly getting older as a result of the success of ART in reducing HIV associated
morbidity and mortality (supported by the fact that the 2016 Ghana HIV sentinel survey report
indicate the 45-49 years group has the highest prevalence of HIV in Ghana of 5.7%, compared
with the estimated national prevalence of 2.4% for all ages (NACP, 2017)) and age is known
risk factor for both hypertension and several cardiovascular diseases, hypertension as a co-
morbidity will increasingly be seen and importantly influence patient management and service
delivery at HIV clinics.
Hypertension tends to cluster in families with strong genetic basis and resultant inherited
biochemical abnormalities. It thus seems like several factors contribute to the pathogenesis of
essential hypertension and these include hereditary, enviromnental, lifestyle and metabolic
with the underlying factor being renal mechanisms (Guyton, 1991). As such hypertension is a
result of an interaction between factors like sympathetic nervous system activity,
overproduction of sodium-retaining hormones and vasoconstrictors, long-term high sodium
intake, inadequate dietary potassium and calcium intake. Other factors implicated in the
pathogenesis of hypertension include increased renin secretion with increased production of
angiotensin II and aldosterone, reduction in the production of vasodilators like prostacycline,
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nitric oxide (NO) and the natriuretic peptides, changes in the kallikrein-kinin system affecting
vascular tones and salt handling by the kidneys, diabetic mellitus, obesity, increased oxidative
stress and endothelial dysfunction. Hypertension has been shown to be related to the risk of
stroke and cardiovascular diseases and have become an important public health challenge and
both hypertension and CVDs have been associated with mortality in PLHIV (Bloomfield et al.,
2014b).
In the present ART era some studies have raised the possibility that ARVs may also induce
hypertension (Chow et aI., 2003; Gazzaruso et al., 2003; Palacios et al., 2006; Coloma Conde
et al., 2008) through acceleration of atherogenesis and the subsequent hardening of the blood
vessel walls (Dube et aI., 2008). Additionally, results from two large prospective cohort studies
(Mary-Krause et aI., 2003; Friis-Moller et al., 2007) indicated that PLHIV are at an increased
risk of CVDs in the long term. However, studies on the association between hypertension and
ART and the role of different treatment regimens in hypertension genesis show conflicting
results. Additionally, although a few of these studies have addressed this issue in SSA, these
studies also show contradictory results in terms of the association between ARV sand
hypertension. Whilst studies in Kenya (Kagaruki et al., 2014), Ghana (Ngala and Fianko, 2014)
and South Africa (Oni et al., 2015) associated hypertension with ART, other studies in Kenya
(Bloomfield et al., 2011), Senegal (Diouf et al., 2012) and South Africa (Edwards et aI., 2015)
found no association between ART and hypertension. However, in all these conflicting studies
on the association between ART and hypertension, the study design and analysis has been
through regression modelling. A systematic review and meta-analysis supports an association
between ART and increased blood pressure and also increased risk of hypertension in PLHIV
(Nduka et aI., 2016a) but there is little evidence whether this association is causal in nature. In
all the studies reviewed (Nduka et al., 2016a), the statistical methods employed in examining
the association between ART and hypertension have been regression modelling which is
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seldom used for causal inferences. However, there is a growing appeal to use statistical
methods in observational studies to infer or rule out causal effect especially in situations where
controlled experiments are not feasible due to ethical reasons (Rosenbaum and Rubin, 1985;
Austin, 2011 a; Austin, 20 11b). Inferences which draw on causal effect continue to evolve in
modern-day epidemiology and causal effect has expanded beyond the "Hill's Criteria for
Causation" (Hill, 1965; Lucas and McMichael, 2005; Mirtz et al., 2009). One of the statistical
methods used in inferring causal effect in observational studies is propensity score-matching
analysis. Unfortunately, a search of the literature indicates a dearth of studies in this analysis
direction to link ART with increased blood pressure and increased risk of hypertension in
PLHIV. A study by Nduka et al. (2016a) using propensity score-matching analysis reported of
a plausible causal average treatment effect of ART on increased blood pressure in PLHIV
(Nduka et al., 20 16b). The present study determined the prevalence of hypertension and used
propensity score-matching analysis to estimate the average treatment effect of ART on blood
pressure and risk of hypertension in patients attending the HIV clinic at the Korle- Bu Teaching
Hospital (KBTH) in Accra.
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1.5 Conceptual framework
Life-style factors: Other chronic conditions:
• Alcohol intake • Diabetes
• Tobacco smoking • Dyslipidemia
• Inadequate physical activity • Metabolic syndrome
• Inadeouate intake of fruits
/
HYPERTENSION
..
I
I
I
HIV/AIDS: Anti- retrovirals:
• Persistent inflammation • Subtle but cumulative toxicity
• Immune dysfunction
Figure 1. Conceptual framework of the interaction between socio-demographic, life-style, anthropometric, metabolic and HIV IART-
related factors in the pathogenesis of hypertension in persons living with HIV.
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The proposed conceptual framework for this study, as shown in figure 1, assumes a
complex interaction between the factors associated with hypertension in HIV infection.
The pathways depicted shows that HIV infection can have both direct and indirect effect
in the pathogenesis of hypertension. In the same context, life-style factors and other
co-morbid conditions can also lead to hypertension.
HIV can directly damage the cells of blood vessels and contribute to atherosclerosis or
hardening of the blood vessels. This has been attributed to the inflammation induced by
the HIV infection or its associated proteins which may promote atherosclerosis and the
formation of high-risk plaques (Hemkens and Bucher, 2014). Also attributed to the HIV
infection is immune dysfunction which occurs due to the reduction in the levels of
CD4+ T-cells and other immune function cells (Hemkens and Bucher, 2014). The
indirect effect of HIV is the use of ARV s. ARV s have been attributed to cause subtle
but cumulative toxicity by their effect on the lipid metabolism, and on mitochondrial
function resulting in changes in body composition, with relative losses in subcutaneous
fat and gains in abdominal adiposity. ARVs such as the PIs may also elevate the risk of
hypertension by raising the level of cholesterol and triglycerides in the blood (Reyskens
et al., 2014). Other metabolic abnormalities attributed to ARVs include dyslipidaemia,
insulin resistance, diabetes and increased inflammatory indices (Hemkens and Bucher,
2014).
In addition to the EIV /ART-related factors, other risk factors like smoking, alcohol
intake, inadequate physical activity, inadequate intake of fruits and vegetables are all
known risk factors for hypertension. These factors in addition to a high body mass index
(BMI) are associated with hypertension in the general population and thus are also
present in PLl-UV. In the context of the complexity in the array of causal effects or
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associated factors, it is highly possible that a combination oflife-style factors, the effect
of infection with the virus, the administration of ARV s and its associated metabolic as
well as inflammatory effects would constitute a strong combination that could
significantly determine the frequency and severity of hypertension in the HIV -infected
population. Fortunately, all factors classified as HIV /ART -related, life-style related and
anthropometric were likely to contribute to the overall burden of hypertension in the
study population, as suggested above, were assessed and accounted for as covariates in
both the propensity score-matching (PSM) analysis and in the regression model
generation which yielded to the benefits of the study. The outcome of this, if proven,
could undoubtedly place HIV /ART related factors as important factors associated with
hypertension in PLHIV.
1.6 Justification
It is well known that some infections increase the risk of certain chronic diseases and
vice versa. Numerous serious morbidities are more likely to occur among PLHIV than
among the general population. Although there are increasing worldwide concerns of
co-morbidity in PLHIV and there is considerable research into the commonly occurring
non-communicable diseases and CVDs among PLHIV in resource-rich settings, less is
known about the burden in resource-limited settings. With an increasing dual burden of
HIV and CVDs in Sub-Saharan Africa, the associations between these diseases and our
understanding of them will become of increased public health importance. In resource-
limited settings, additional research is needed to better understand their risk and impact
and identify optimal models of care to address this challenge in the areas where the
majority of older PLHIV will be receiving care. It is important to understand the
incidence rate of these CVDs among PLHIV, along with factors and outcomes
associated with them, in order to guide clinical care and planning of health systems.
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Given the importance and the urgency in adopting preventive measures for
cardiovascular damage among PLHIV on ART, operational research on the factors
associated with hypertension as a risk factor for CVDs in PLHIV would be significant
in policy formulation, implementation and practice. Hence, determining the factors
associated with hypertension, preventing the occurrence of hypertension and treating
hypertension will be clinically relevant issues in PLHIV. In addition, the management
of HIV and other co-morbidities in PLHIV is complicated with the issue with
polypharmacy which can invariably lead to an increased rate of drug-drug interactions
and facilitate drug toxicity and poor medication adherence (De Socio et aI., 2014). This
is supported by the fact that the European AIDS Clinical Society in its 2011 guidelines,
warns of potentially significant interactions of PIs and NNRTIs with antihypertensive
medications calcium-channel blockers and ~-blockers (De Socio et al., 2014). It must
however be said that even though the benefit of ART clearly outweighs the possible
complications associated with it, the progressive aging of the HIV -infected population
and the expected long-term use of ART will come with its own issues.
An evaluation of the factors associated with hypertension as a co-morbid condition in
PLHIV and an estimation of CVD risk score in the Ghanaian population is very
important. This will help define the scope of the problem, characterize the burden of
CVDs among PLHIV and also understand the impact of modifiable risk factors,
Outcomes from this study will help identify individuals with early signs of hype liens ion
who could benefit from interventions to prevent or delay the onset of complications and
thereby improve the overall quality of life of PLHIV. This thesis explored the nature of
the relationship between HIV /ART and hypertension in patients attending HIV clinic
at the KBTH using both a propensity score-matching analysis approach and regression
modelling approach and also calculated the CVD risk score for each patient.
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1.7 Objectives
1.7.1 General Objective
To identify factors associated with hypertension among patients attending the HIV
clinic at the KBTH in Accra.
1.7.2 Specific Objectives
1. To determine the prevalence of hypertension among patients attending the
HIV clinic at the KBTH.
2. To identify lifestyle, anthropometric, metabolic and HIV/ART-related
factors associated with hypertension among HIV -positive patients.
3. To estimate average treatment effect of ART on hypertension among HIV-
positive patients.
4. To estimate and classify cardiovascular disease risk score for HIV -positive
patients.
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CHAPTER TWO
2.0 LITERATURE REVIEW
2.1 Hypertension
Blood pressure refers to the force against the walls of the arteries as the heart pumps
blood through the body. Hypertension is a long-term medical condition in which the
arteries have persistently elevated blood pressure (Naish and Syndercombe Court,
2014). This elevated blood pressure makes the heart to work harder in pumping blood
throughout the body and contributes to the hardening of the arteries (atherosclerosis)
and to the development of several CVDs. Blood pressure is reported using two
measurements; a systolic blood pressure (sBP) and a diastolic blood pressure (dBP).
Hypertension is defined using standard definitions by the World Health Organization
(WHO)!International Society of Hypertension (ISH) guidelines: diastolic blood
pressure (dBP) values of~90 mmHg or systolic blood pressure (sBP) values of~140
mmHg at 2 or more clinic visits (Whitworth, 2003). Hypertension has become an
important public health challenge in both developed and developing countries (Kearney
et al., 2004). Hypertension has now become an increasingly common health problem
which is attributable to several risk factors including, race, family history, sedentary
lifestyle, smoking, lack of physical activity, high levels of salt intake, high levels of
alcohol consumption, tobacco use, being overweight or obese, stress and certain chronic
medical conditions including kidney diseases (Yusuf et a!., 2001). In the absence of
broad and effective preventive measures in developing countries, the prevalence of
hypertension is expected to increase (Chobanian et al., 2003).
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2.1.1 Signs and symptoms of hypertension
Symptoms of hypertension are rarely seen although reports of headaches, light-
headedness, vertigo, tinnitius and altered vision or fainting episodes are often reported
(Fisher and Williams, 2005). Hypertension has also been associated with hypertension
retinopathy, changes in the optic fundus of the eye of which the severity of the
retinopathy can be used as an estimate of the duration and/or severity of the
hypertensive state (Fisher and Williams, 2005).
2.1.2 Types of hypertension
There are two types of hypertension defined by the underlying cause of the elevated
blood pressure. These are primary or essential hypertension and secondary
hypertension.
2.1.2.1 Primary hypertension
Primary hypertension also referred to as "essential hypertension" is defined as high
blood pressure without any clearly defined aetiology affecting the blood pressure
regulating mechanism (Williams, 2015). It is the most common form of hypertension
accounting for 97-98% of all hypertensive cases (Oparil et al., 2003) and practically its
treatment results in lower blood pressure results and significant clinical benefits
(Williams, 2015). Most people with essential hypertension show no symptoms, but
symptoms like frequent headaches, tiredness, dizziness and nose bleeding has been
reported in some cases. The cause of essential hypertension is unknown but factors like
obesity, smoking, alcohol abuse, excess salt intake diet and heredity has been associated
with the condition (Poulter et al., 2015).
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2.1.2.2 Secondary hypertension
Secondary hypertension refers to a type of hypertension in which the underlying cause
is specific and it usually involves the renal or endocrine system (Williams, 2015).
Secondary hypertension accounts for 2-3% of patients with hypertension (Ilyas, 2009).
Renal causes of secondary hypertension include glomerulonephritis, diabetic
nephropathy, polycystic kidney disease and renal stenosis (Ilyas, 2009). Some diseases
of the endocrine system like Cushing's syndrome, Conn's syndrome
(hyperaldosteronism), phaecochromocytoma, thyroid dysfunction and over activity of
the adrenal gland can lead to secondary hypertension (Ilyas, 2009; Rodriguez et al.,
2010). Other factors attributed to secondary hypertension include sleep apnoea,
pregnancy associated hypertension, alcohol abuse, some herbal medicines and the use
of illicit drugs (Ilyas, 2009; Rodriguez et al., 2010; Grossman and Messerli, 2012).
Arsenic contaminated drinking water has also been linked to secondary hypertension
(Abhyankar et al., 2012; Farzan et aI., 2015; Jiang et al., 2015).
2.1.3 Pathogenesis and pathophysiology of essential hypertension
Essential hypertension tends to cluster in families with strong genetic basis and
resultant inherited biochemical abnormalities. It thus seems like several factors
contribute to the pathogenesis of essential hypertension and these include hereditary,
environmental, lifestyle and metabolic with the underlying factor being renal
mechanisms (Guyton, 1991). As such, essential hypertension is a result of an interaction
between genetic factors and environmental factors. In an appraisal of the various
implicated or suggested mechanisms of the pathogenesis of essential hypertension,
Oparil et al. (2003) noted several mechanisms including increased sympathetic nervous
system activity, overproduction of sodium-retaining hormones and vasoconstrictors,
long-term high sodium intake, inadequate dietary potassium and calcium intake, and
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increased renin secretion with increased production of angiotensin II and aldosterone
(Oparil et al., 2003). Other mechanisms implicated are diabetic mellitus, insulin
resistance, obesity, increase in the activity of vascular growth factors, changes in the
adrenergic receptors affecting heart rate and vascular tone, increased oxidative stress,
endothelial dysfunction and vascular remodelling (Oparil et al., 2003).
2.1.4 Measurement of blood pressure in adults
Measurement of blood pressure is in millimetres of mercury (mm Hg) recorded as
systolic blood pressure and diastolic blood pressure. These two numbers are usually
written one above the other with the upper munber being the systolic blood pressure
reading and the lower number being the diastolic blood pressure reading. Systolic blood
pressure is the highest pressure in blood vessels when the heart contracts whilst diastolic
blood pressure is the lowest pressure in blood vessels in between heart beats when the
heart relaxes (WHO, 2013a). The WHO defines normal blood pressure in an adult as a
systolic blood pressure of 120 mm Hg and a diastolic blood pressure of 80 mm Hg but
with the recognition that the cardiovascular benefits of normal blood pressure extends
to systolic blood pressure of 105 mm Hg and diastolic blood pressure of 60 mm Hg
(WHO, 2013a). The WHO defines hypertension as a systolic blood pressure 2:140 mm
Hg and/or diastolic blood pressure 2:90 mm Hg (WHO, 2013a).
There are basically three different devices that can be used to measure blood pressure;
electronic, mercury and aneroid devices (WHO, 2003; WHO, 2013a). The World
Health Organisation recommends the use of affordable and reliable electronic devices
that have the option to select manual readings (WHO, 2003; Parati et al., 2010; WHO,
2013a).
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According to the National Institute for Health and Care Excellence (NICE) in its 2011
guidelines, hypertension should be diagnosed based on readings of blood pressure done
on three separate occasions with at least one monthly intervals (NICE, 2011). First
published in 2004 and subsequently reviewed in 2006, 2008, 2009 and 2011, the NICE
guidelines distinguishes three types of blood pressure measurements available for the
diagnosis of hypertension as follows;
• Clinic blood pressure monitoring (CBPM)
• Ambulatory blood pressure monitoring (ABPM) and
• Home blood pressure monitoring (HBPM)
In the measurement of blood pressure, health workers must ensure that all devices used
are properly validated, maintained and regularly recalibrated to meet manufacturer's
instructions (NICE, 2011). This is in addition to ensuring that the environment for the
measurements should be standardised, relaxing and the person should be quiet, seated,
arm outstretched and supported (NICE, 2011).
Both CBPM and ABPM are used in the diagnosis of hypertension but with ABPM
identified as the most accurate and cost-effective means of confirming the diagnosis
(NICE, 2011; McCormack et al., 2012; Siu, 2015). For CBPM, two measurements are
recommended and if the second is substantially different from the first, a third
measurement is done and the lower of the last two measurements used as the blood
pressure readings (NICE, 2011; McCormack et al., 2012). In ABPM, the recommended
protocol involves taking blood pressure measurements at least twice hourly during the
persons wake hours (e.g. between 8.00 am and 10.00 pm) in a day (NICE, 2011;
McCormack et al., 2012). Thereafter, the diagnosis of hypertension is made based on
the average of 14 readings taken during the period.
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In situations where ABPM is unsuitable or intolerable, then HBPM is used. This
involves taking blood pressure readings twice daily (in the morning and evening) for 4-
7 days and calculating the average but making sure to ignore the first day's readings in
the calculations (NICE, 2011; McCormack et al., 2012)
2.1.5 Diagnosis and stages of hypertension in adults
According to the NICE guidelines, diagnosis of hypertension is divided into 3 stages
depending on the type of measurements done (NICE, 2011);
• Stage I hypertension: CBPM systolic blood pressure ~140 mmHg and/or
diastolic ~90 mmHg and subsequent ABPM or HBPM values of systolic
blood pressure ~135 mmHg and/or diastolic blood pressure ~85 mmHg.
• Stage II hypertension: CBPM systolic blood pressure ~160 mmHg and/or
diastolic ~100 mmHg and subsequent ABPM or HBPM values of systolic
blood pressure ~150 mmHg and/or diastolic blood pressure ~95 mmHg.
• Stage III hypertension (Severe hypertension): CBPM systolic blood
pressure ~180 mmHg and/or diastolic ~110 mmHg.
Guidelines of the American Heart Association recommend a minimum of three blood
pressure measurements done on at least two separate hospital visits (Aronow et al.,
2011). The 7th report of the Joint National Committee on Prevention, Detection,
Evaluation and Treatment of high blood pressure (JNC 7) classifies blood pressure
readings as follows (Chobanian et al., 2003);
• Normal: Systolic blood pressure readings of 90-119 mmHg and/or diastolic
blood pressure readings of 60-79 mmHg
• Pre-hypertension: Systolic blood pressure readings of 120-139 mmHg
and/or diastolic blood pressure readings of 80-89 mmHg
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• Stage I hypertension: Systolic blood pressure readings of 140-159 mmHg
and/or diastolic blood pressure readings of 90-99 mmHg.
• Stage II hypertension: Systolic blood pressure readings of 160-179 mmHg
and/or diastolic blood pressure readings of 100-109 mmHg.
• Isolated systolic hypertension: Systolic blood pressure readings of 2:140
mmHg and diastolic blood pressure readings <90 mmHg.
2.1.6 Prevention of hypertension
Strategies adapted to prevent hypertension are usually population based, as this will
invariably reduce the intake of antihypertensive medications. In general, most programs
to reduce the incidence of hypertension involve lifestyle modifications. Behavioural
factors play an essential role in the incidence of hypertension. A policy statement from
the World Hypertension League in 2014 indicated the following facts (Campbell et al.,
2014);
• Unhealthy diet is major factor estimated to contribute to about half of all
hypertensive cases
• An estimated 30% of hypertensive cases are related to increase in salt intake
• About 20% of hypertensive cases are related to insufficient intake of dietary
potassium
• Lack of effective exercise is related to about 20% of hypertensive cases
• Obesity accounts for about 30% of hypertensive cases and
• Excessive consumption of alcohol is also associated with hypertension
The British Hypertensive Society and the U.S. National Education Programme all
focuses on lifestyle modification as these modifications may lower blood pressure as
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much as will individual antihypertensive medications (Whelton et al., 2002; Williams
et al., 2004). Their programmes are focused on the following indices;
1. Reduction and subsequent maintenance of body mass index (BMI) to be
between 20 and 25 kg/rrr'
2. Reduction of dietary salt intake «6g of salt/day)
3. Participation in regular aerobic physical activity or exercising (~30
minutes/day)
4. Limit to alcohol intake (not more than 3 units/day for men and 2 units/day
for women)
5. Ensuring fruit and vegetable become part of the daily dietary intake (at least
5 portions/day)
The WHO in consonant with advocating bodies recommends population-wide policy
interventions to reduce the incidence of hypertension. These interventions should
include policies addressed to reduce the harmful use of alcohol, increase activity
through exercises, reduction in overweight and obesity, reduce the intake of dietary salt,
stopping tobacco use and exposure and managing stress (UNAIDS, 2015).
2.1. 7 Management of hypertension
The management of hypertension involves lifestyle modifications (as in the prevention
of hypertension) and the use of antihypertensive medications. Different guidelines
indicate target blood pressure readings for the general population and patients with co-
morbidities like diabetes and kidney disease in the management of hypertension. Other
guidelines suggest different target blood pressure readings for patients aged over 60
years and those aged over 80 years. In most guidelines the recommended target for the
general population is between 140 and 160 mmHg for systolic blood pressure and 90-
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100 mmHg for diastolic blood pressure (Argued as et al., 2009; NICE, 2011; Mancia et
al., 2013; James et al., 2014; Daskalopoulou et al., 2015).
Several classes of antihypertensive medication are available for the management of
hypertension and these include diuretics, calcium channel blockers (CCBs),
angiotensin-converting enzyme (ACE) inhibitors, angiotensin II receptor blockers
(ARBs), adrenergic receptor antagonists, vasodilators, benzodiazepines, renin
inhibitors, aldosterone receptor antagonists, 0,-2 adrenergic receptor agonists and
endothelin receptor blockers. First line antihypertensive medications include thiazide-
type diuretics, calcium-channel blockers, ACE inhibitors and angiotensin II receptor
blockers (ARBs) (James et al., 2014). The diuretics increase the secretion of excess salt
and water by the kidneys from the body whilst the CCBs block the entry of calcium
into muscle cells of the artery walls. The ACE inhibitors inhibit the activity of
angiotensin converting enzyme and the ARBs work by antagonising the activation of
angiotensin receptors. Drugs from the various classes are used alone or in combination
but with a majority of cases needing more than one medication in the management of
hypertension (Go et aI., 2014).
2.1.8 Epidemiology of hypertension worldwide
In the year 2000, about 25% (estimated to be 972 million) of the world's adult
population were estimated to be have hypertension defined as either a blood pressure
reading of ~140/90 mmHg or in the use of antihypertensive medication (Williams,
2015). The WHO estimates this proportion will hit 29% (representing about 1.6 billion)
i.e. a prevalence of 60%, by the year 2025 (Williams, 2015). This dramatic increase in
the worldwide prevalence of hypertension is expected to come primarily from the
economically developing countries contributing to an estimated 75% of the world's
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hypertensive population by 2025 (Kearney et al., 2005; Williams, 2015). This is said
will continue to maintain hypertension at its position as the World's single most
important preventable cause of death prematurely over the next 2 decades with an
estimate of 7.1 million deaths/year (Williams, 2015). The higher the blood pressure, the
higher the risk of heart and blood vessel damage to the brain and kidneys. As such,
hypertension causes about one-half of all deaths from stroke and heart diseases (WHO,
2008b; WHO, 2015).
In 2013, all the 194 states of the WHO agreed on measures to reduce avoidable non-
communicable diseases with nine voluntary global targets. The 6th of these targets is to
reduce by 25% the global prevalence of elevated blood pressure between 2013 and 2030
(WHO, 20 13b). This intervention and declaration is important, as elevated blood
pressure is rated as the leading risk factor for death and disability worldwide (Lim et
al., 2012). A systematic review of disease burdens indicated that globally, elevated
sBP >110-11 5 mmHg is the leading risk factor for both mortality (9.4 million deaths)
and morbidity (7% of daily-adjusted life years) (Lim et al., 2012). In addition, a joint
policy statement from the World Hypertension League and the International Society of
Hypertension released in 2014 also emphatically implicated hypertension as causing an
estimated 9.4 million deaths and 162 million years of life lost in 2010 (Campbell et al.,
2014). The statement also implicated hypertension as disproportionately impacting on
low and middle-income countries (LMIC) with two-thirds of cases being in
economically developing countries and the fact that heart disease and stroke is more
common in economically-developing countries than the developed countries (WHO,
2013b; Campbell et al., 2014).
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Although figures indicate that the global age-standardised blood pressure has decreased
within the past 30 years (1980-2008), the population of persons with hypertension has
increased from 605 million to 978 million which has been attributed to both population
growth and ageing of most populations (Danaei et al., 2011).
Studies have indicated an association between elevated blood pressure and
socioeconomic status. A cohort study involving 27,000 women showed a lower
socioeconomic status, lower education and lower income were associated with the
development of hypertension (Conen et aI., 2009). Two major studies in this area are
the WHO Study on Global Ageing and Health (SAGE) and the Prospective Urban Rural
Epidemiology (PURE) study. Interestingly figures from SAGE indicate comparably
age-standardised hypertension (defined as sBP 2:140 mmHg and/or dBP 2:90mmHg)
among people aged >50 years in low and middle-income countries and high-income
countries (Lloyd-Sherlock et al., 2014). The PURE study showed that in LIC, 30% of
urban hypertension patients receive anti-hypertension medication but only 20% of the
rural patients receive anti-hypertensive medication (Chow et al., 2013). The PURE
study also indicated difference between LMIC and HIC such that in HIC, men have
significantly higher rates of hypertension than women do, but in LMIC, the reverse is
true (Teo et al., 2009). The PURE study also showed that the worldwide rate of
hypertension awareness among hypertension patients was 47% with 13% being able to
control their blood pressure levels to <140/90 mmHg (Chow et al., 2013). The PURE
study results that, whilst 32% of hypertension patients in LIC were treated for the
condition with 13% been able to achieve blood pressure levels of <140/90 mmHg is
comparable to the SAGE study which showed rates of blood pressure control in LIC is
low ranging from 4% in Ghana to 14% in India (Chow et al., 2013; Lloyd-Sherlock et
al., 2014).
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In SSA, hypertension and other cardiovascular diseases continue to be rated as the
leading causes of mortality and disability (Lawes et a1., 2006; WHO, 2013b). An
African Union report in 2006 stated that hypertension is next to HIV /AIDS in posing
the greatest challenge to the healthcare systems in most Africa countries (WHO and
AU, 2006). Africa is also reported to have a higher burden of hypertension compared
with other world regions but the extent of this burden in most countries is still unknown
(Addo et a1., 2007; WHO, 2013b). In the year 2000, the WHO reported a hypertension
prevalence of 46% in the African region which was highest globally (WHO, 2011a).
Although this prevalence is higher than what has been projected and reported by most
studies it is generally accepted that the prevalence of hypertension in SSA is rising.
This increase is being seen more in young and active Africans and this has been
attributed to several factors including the ageing of most African population, increasing
populations, rapid urbanization and increasing rural-urban migration (Opie and Seedat,
2005; de-Graft Aikins et a1., 2010). A systematic review involving 92 selected studies
on hypertension in Africa indicated an overall pooled crude prevalence of hypertension
of 19.7% in 1990, 27.4% in 2000 and 30.8% in 2010 and a pooled awareness rate of
16.9% in 1990,29.2% in 2000 and 33.3% in 2010 (Adeloye and Basquill, 2014). With
the exception of 1990, in all the other years, the rates were comparatively higher in
females than males. Using modelling estimates the study further reported of an estimate
of 54.6 million hypertensive cases in 1990 (age-adjusted prevalence of 19.1%), 92.3
million cases in 2000 (age-adjusted prevalence of 24.3%), 130.2 million cases in 2010
(age-adjusted prevalence of 25.9%) and a projection of 216.8 million cases by 2030
(age-adjusted prevalence of25.3%) (Adeloye and Basquill, 2014).
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These results indicate global differences between HIC and LMIC with major shortfalls
in the awareness of hypertension, uptake of anti-hypertensive medication and the
subsequent control of the elevated blood pressure (Rahimi et aI., 2015).
2.1.9 Epidemiology of hypertension in Ghana
In 1979, hypertension was described as an epidemic in Ghana (Pobee et al., 1979).
Several studies including two systematic reviews by Bosu (2010) and Addo et al. (2012)
have been conducted in the epidemiology of hypertension in Ghana. Since then,
hypertension has been rated as the third most common cause of mortality in Ghana
(Owusu-Sekyere et al., 2013). A review of the literature indicates several population
studies have been conducted on the prevalence of hypertension in different sub-
populations in Ghana. Most of these studies were conducted in the Greater Accra
Region and Ashanti Region of Ghana (the two most populous regions in Ghana), with
a few in the Volta Region and Upper East Region. These studies were conducted in
urban or rural communities. With the exception of the studies by Pobee et al. (1977 and
1979), Ikeme et al. (1978) and Chukwuemeka et al. (1983) which used a criteria of
hypertension of sBP 2:160 and/or dBP 2:95, the rest of the studies used a criteria of
hypertension of sBP 2:140 and/or dBP 2:90.
A study conducted in 1972 by Pobee et al. (1979) involving 6900 study participants
(public servants-urban dwellers) in the Greater Accra Region indicate a hypertension
prevalence of8.9%. Studies conducted in 1975 by Ikeme et al. (1978) and in 1981 by
Chukwuemeka et al. (1983) indicate a hypertension prevalence of 13.1% and l3.0%
respectively in an urban community in the Greater Accra Region. Other studies
conducted in 1998 by Amoah et al. (2003) (study participants of 4733), in 2000 by
Escalona et al. (2004) (study participants of 598) and in 2005 by Addo et al. (2008)
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(study participants of 1015) indicated a hypertension prevalence of28.3%, 26.8% and
30.2% respectively in urban communities in the Greater Accra Region. Studies
conducted in other urban communities in other regions in Ghana have shown similar
results; Agyeman et al. (2006) indicated a hypertension prevalence of 29.4% (1431
study participants) in a study conducted in 2004 in Kumasi Metropolis (urban
community) plus four rural communities.
Population studies on hypertension prevalence have also been conducted in rural
communities in the Greater Accra Region. Studies conducted in 1973 by Pobee et al.
(1977) in 20 rural communities in the Greater Accra Region (study participants of 1670)
indicated hypertension prevalence of 4.5% (criteria of hypertension of sBP 2:160 and/or
dBP 2:95). Another study conducted in 2001 by Addo et al. (2006) in four rural
communities in the Greater Accra Region (study participants of 362) indicated
hypertension prevalence 25 .4% (criteria of hypertension of sBP 2:140 and/or dBP 2:90).
Other studies conducted in rural communities in other parts of Ghana indicate
comparable hypertension prevalence. A study conducted in the Volta region in 2002 by
Burket et al., (2006) indicated a hypertension prevalence of 32.8% (287 study
participants). Other studies conducted by Cappucio et al. (2004) in 2001 in the Ashanti
region and Kunutsor & Powles (2009) in 2007 in the Upper East region indicate
hypertension prevalence of 28.7% (1013 study participants) and 19.2% (574 study
participants) respectively.
In 2008, the Ghana Health Service reported that the incidence of hypertension increased
from 49,087 persons in 1988 to 505,180 persons in 2007 (a span of two decades) and
also the proportion of hypertension in outpatients relative to other reported diseases
increased from 1.7% to 4.0% (GHS, 2008). In Ghana, the Ministry of Health and the
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Ghana Health Service have reported that hypertension ranked second as the most
common medical condition and cause of outpatient morbidity and attributed it to poor
unhealthy lifestyle of most urban dwellers (MOH, 2005; GHS, 2012). Figures from the
2011 annual report of the Ministry of Health indicate a prevalence of hypertension
between 30% and 40% and that, at the Korle-Bu Teaching Hospital (the premier tertiary
hospital in Ghana), hypertension and related condition is responsible for about 70% of
all cases of death and constitutes more than 50% of cases on admission (MOH, 2011).
A systematic review of 17 epidemiological studies in Ghana reported in 2010 indicated
that most studies reported crude hypertension prevalence between 25% and 48% with
a higher prevalence in males compared with females (Bosu, 2010). The study indicated
an epidemic of hypertension in Ghana and reported that the prevalence of hypertension
increased from about 25%-28% in the period 1976-1998 to about 37%-45% between
2002 and 2006 (Bosu, 2010). In addition, the study reported of higher prevalence of
hypertension among urban dwellers compared with rural dwellers and an association
between hypertension and age, BMI, alcohol consumption and educational level (Bosu,
2010). The review also reported that between 22% and 54% of hypertension patients
had knowledge of their condition and 7%-31 % were on medication (Bosu, 2010).
Another systematic review by Addo et al. (2012), involving 11 population studies on
hypertension conducted between 1973 and 2007 indicated that the mean sBP was 122.0-
139.4 mmHg in women and 123.8-132.9 mmHg in men. The review also reported that
the mean dBP was 68.8-86.4 mmHg in women and 69.2-78.4 mmHg in men. The
prevalence of hypertension from the review was between 19.3% and 54.6% using a
criteria of hypertension of blood pressure readings of> 140/90 mmHg CAddo et aI.,
2012). The review also reported a consistent difference in the prevalence of
hypertension between urban populations and rural populations. Addo et al. (2012), also
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indicated that hypertension was associated with increasing age, BMI, waist
circumference and pulse rate. Other factors associated with hypertension from the
review were excessive alcohol and salt consumption and a family history of the
condition (Addo et al., 2012). However, factors like physical activity and socio-
economic status were either not determined or the results inconclusive (Addo et al.,
2012). In terms of treatment and control, the review indicated low rates of detection,
treatment and control of hypertension and further reported of differences in these
indicators between urban populations and rural populations (Addo et al., 2012).
Although several hypertension prevalence studies have been conducted in Ghana, the
2014 Ghana Demographic and Health Survey (GDHS) was the first national survey to
include measurements of blood pressure among adults aged 15-49 year. The national
survey indicated a hypertension prevalence of 13.0% (GSS, 2015). The survey also
reported of a higher prevalence of hypertension in urban areas (16.0%) compared with
rural areas (9.0%) (GSS, 2015). In addition, the survey also reported an association
between hypertension and both increasing age and increasing wealth (GSS, 2015).
Interestingly, although the survey instrument indicated that a high proportion of study
participants with hypertension were unaware of their blood pressure condition (63% in
women and 86% in men).
Conclusively, these studies point to an epidemic of hypertension 111 Ghana with
increasing prevalence but low levels of control and management.
2.2 Human Immunodeficiency Virus (HIV) and Acquired Immune
Deficiency Syndrome (AIDS)
Acquired immune deficiency syndrome (AIDS) IS caused by the human
immunodeficiency virus (HIV). Upon infection with the virus, an infected person is
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said to sero-convert with an activation of the immune system to produce antibodies to
the virus and this is followed by a latent or asymptomatic stage. If undetected and/or
umnanaged, there is progressive immunosuppression and the latent stage gradually
leads to a symptomatic stage with the occurrence of opportunistic infections and
malignancies. In the last stages of the infection, the infected individual is said to have
AIDS or HIV disease. Although currently there is no vaccine against HIV, available
therapeutic management involves the use of ART which can delay an infected
individual's suppression of the immune system and also control the occurrence of
opportunistic infections. With the implementation of ART, HIV -positive patients can
now boast of a considerable survival pattern and may now have a near-normal life
expectancy (UNAIDS, 2012; CDC, 2015). The HIV infection which was formerly seen
as a fatal disease when it emerged is now more of a chronic disease when managed
appropriately and timely (Braitstein et al., 2006; Hall et al., 2006).
2.2.1 Background to the HIV /AIDS pandemic
HIV/AIDS is deemed to have originated during the mid-20th century from sub-Saharan
Africa (Gao et aI., 1999) and is considered as a global pandemic- a disease, which is
widespread in geographical distribution and actively spreading (Cohen et al., 2008;
Kallings, 2008). The recognition of AIDS as a new disease was reported by Centres for
Disease Control and Prevention (CDC) in 1981 when a rare form of pneumonia caused
by Pnemocystis jiroveci (previously called Pneumocystis cariniii and other
opportunistic infections and rare malignancies were reported in young homosexual men
(CDC, 1981). This was followed by a series of reports of similar immune deficiency
syndromes in several diverse populations (CDC, 1982; Masur et al., 1982; CDC, 1983;
Clumeck et aI., 1983). In 1983, a group of scientists working at Pasteur Institute in
France led by Luc Montagnier described the causative organism of AIDS as
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lymphadenopathy-associated virus (LAV) (Barre-Sinoussi et al., 1983). This was later
confirmed by another group of scientists working in the United States led by Robert
Gallo, then working with the National Cancer Institute (part of National Institute of
Health, U.S.A) who described it as Human T lymphotropic virus type III (HTLV-III)
(Gallo et al., 1984). Thereafter after much consultations and discussions, the virus was
renamed human immunodeficiency virus (HIV) (Coffin et al., 1986a; Coffin et al.,
1986b).
2.2.2 Global HIV/AIDS epidemiology
Since the HIV 1AIDS pandemic broke out in the early 1980's, it has increased the disease
burden of countries and has been one of the leading causes of both mortality and
morbidity such that in sub-Saharan Africa, life expectancy has been reduced by more
than 20 years, economic growth slowed, and household poverty deepened
(UNAIDS/WHO, 2008).
Estimates indicate that globally, HIV is the leading cause ofDAL YS among people age
30-44 years (Ortblad et al., 2013). With an increasing burden of the disease in most
countries, HIV 1AIDS is considered a major global public health issue and the "single
greatest reversal in human development" in modern history (UNDP, 2005; Maartens et
al., 2014). From the year 2000 to 2014, it was estimated that 38.l million people were
infected with HIV and 25.3 million people died from AIDS-related illnesses (UNAIDS,
2015). However, the incidence of new infections has decreased by 35% from 3.1
million in 2000 to 2.0 million by the end of 2014 and an estimated prevalence of 36.9
million people as at the end of2014 (UNAIDS, 2015). In addition, the decrease in the
incidence of HIV between 2000 and 2014 was accompanied by a decrease of 24%
globally in AIDS-related deaths from a peak of 2.0 million deaths in 2004/2005 to 1.2
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million deaths in 2014 (UNAIDS, 2015). This has been attributed to a concerted effort
of all countries in achieving the Millennium Development Goals (MDGs) on
HIV/AIDS and the introduction of ART (UNAIDS, 2015).
Although with an estimated population of 10% of the world's population, sub-Saharan
African (SSA) carries the heaviest burden of the HIV/AIDS pandemic (Aryee, 2009).
In most countries in SSA where the disease has a high prevalence, the socio-economic
impact of HIV IAIDS includes a decrease in life expectancy, a reduction in economic
growth and an increase in household poverty levels (UNAIDS and WHO, 2008; Aryee,
2009). Estimates as at the end of 2014 indicate 25.8 million people with HIV/AIDS
live in SSA with 57% being women (UNAIDS, 2015). In addition, an estimated 1.4
million new infections were accountable to SSA (UNAIDS, 2015) but this is a decrease
of about 41% between 2000 and 2014 (UNAIDS, 2015). Despite this progress, SSA
still leads in the global proportion of total new infection (UNAIDS, 2015). AIDS-
related deaths in SSA in 2014 were estimated to be 790,000 people representing 34.2%
of the global estimate but this is seen as a decrease by 48% between 2004 and 2014
(UNAIDS, 2015).
Generally, the number of patients with access to ART has increased over the years; 6.1
million people in 2009 to 14.9 million people in 2014 (UNAIDS, 2015). Globally, as at
June 2015, 15.8 million PLHIV were assessing ART (UNAIDS, 2015). Within the SSA
sub-region, 10.7 million PLHIV were receiving ART in 2014, which accounts for
41.0% of all HIV patients in the sub-region (UNAIDS, 2015). This proportion is made
up of 47% of women and 36% of men. Although the access to anti-retroviral in the SSA
is still below the expected proportion, it is a vast improvement over the fewer than a
100,000 people assessing ART in 2002 (UNAIDS, 201,§2tJ I SCHr . r-
,,-'V" I . ,)1O.L..- ,O' r- PUBLIC.",:~':l.,.; ~_J~:'\
~~.'J) 'L.}- .. I H L.t B i-OH
-""ly
...--........\.
'. - :-,:-':C- LEG 0 N
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2.2.3 HIV classification and sub-types
HIV, the virus that causes AIDS is characterised into two main types; HIV -Type
(HIV -1) and HIV -Type 2 (HIV -2). HIV -1 is assumed to have been transmitted from
apes whilst HIV-2 is from sooty mangabey monkeys (Sharp and Hahn, 2011; Maartens
et al., 2014). Four different groups of HIV-I have been isolated namely group M, N, 0
and P (Maartens et al., 2014). Whilst groups N, 0 and P are restricted to the West
Africa sub-region, group M is widespread and is the cause of the global HIV pandemic
(Maartens et al., 2014). HIV-1 group M is further characterised into nine (9) subtypes;
A, B, C, D, F, G, H, J and K of which sub-type C is the dominant type found in Africa
and India and accounting for an estimated 48% of all HIV -1 infections worldwide in
2007 (Sharp and Hahn, 2011). Apart from these nine sub-types of HIV -1, studies
indicate circulating recombinant forms (CRFs) of sub-type M are becoming common
(Maartens et al., 2014).
HIV -2 is predominantly confined to the West-Africa sub-region. It is made up of seven
(7) groups (A, B, C, D, E, F and G) with Groups A and B being the prominent groups
found in most individuals with HIV -2 infection (Clavel et al., 1986a; Trevino et al.,
2011). Although both HIV -1 and HIV -2 immunodeficiency illness progresses to AIDS
if unmanaged, the rate of progression for HIV -2 is slower compared with HIV -1 and
less transmissible as an infection (Sharp and Hahn, 2011). The underlying basis for
these differences in their disease progression remains unclear but it has been suggested
it has to do with the virus itself and the host of the infection (Clavel et al., 1986b; Schim
van der Loeff and Aaby, 1999).
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2.2.4 HIV transmission and risk factors
HIV is transmitted mainly through three (3) different routes; unprotected sex (including
anal sex), contaminated blood transfusions and hypodermic needles and mother-to-
child transmission during pregnancy, delivery or through breastfeeding (Markowitz,
2007).
2.2.4.1 Transmission of HIV through sexual intercourse
Unprotected sexual contact is the most frequent mode of HIV transmission with the
majority been through heterosexual intercourse (Markowitz, 2007). In both
heterosexual and homosexual contacts, the risk of infection through anal intercourse is
high, estimated at 1.4-l.7% per act (Boily et aI., 2009; Beyrer et aI., 2012). In low-
income countries, the risk of transmission in heterosexual contacts is estimated at
0.34% per act, which is far higher than the estimated 0.06% per act in high-income
countries (Boily et aI., 2009). Although, only a few cases of oral sex transmission have
been reported, the risk of infection through oral sex is still considered a possibility
(Pattman, 2010; Yu and Vajdy, 2010).
Factors associated with increased risk of sexual transmission of HIV include the viral
load (2.4 fold-increased risk per 1 10giOincrease in viral load), presence of other
sexually transmitted infections and genital ulcers, pregnancy and anal intercourse
(Maartens et aI., 2014). Other factors are number of sexual partners and concurrent
sexual partnerships (Maartens et aI., 2014).
2.2.4.2 Transmission of HIV through contaminated blood/blood products
Transmission of HIV through contaminated blood and blood products including blood
transfusion and its products, sharing of hypodermic needles during intravenous drug
usage is the second most frequent mode of HIV transmission (Markowitz, 2007). In
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low-income countries, it is estimated that transmission through this route accounts for
15% of all new infections which is estimated to be 5-10% of all global infections
(Markowitz, 2007).
2.2.4.3 Mother-to-child transmission of HIV
Mother-to child transmission, also termed vertical transmission can be during
pregnancy, childbirth or breastfeeding. It is the third most frequent route of HIV
transmission globally (Markowitz, 2007). In the absence of ART, the risk of
transmission of HIV from an infected mother to a child during pregnancy or childbirth
is estimated to be 20% whilst transmission through breastfeeding is estimated at a 35%
risk (Coutsoudis et al., 2010). Policies targeted at ensuring every pregnant woman is
screened during antenatal care have greatly reduced the rate of HIV infection through
this route.
2.2.5 HIV life-cycle and pathogenesis
HIV, the causative organism of AIDS is a retrovirus belonging to the family
Retroviradae and the genus Lentivirus (ICTV, 2002). Lentiviruses characteristically do
have long incubation periods with accompanying long disease duration (Levy, 1993).
They are transmitted as single-strand positive-sense enveloped viral RNA, which upon
entry into the host cell is transcribed into a double-stranded DNA, enters into the
nucleus and is integrated with the hosts cellular DNA. Figure 2 shows a schematic
diagram of HIV showing the HIV envelope with the protein spikes and the capsid
containing the HIV nucleic acid and enzymes (AIDSinfo, 2016). There are seven stages
of the HIV life cycle namely: 1. binding, 2. fusion, 3. reverse transcription, 4.
integration, 5. replication, 6. assembly, and 7. budding (AIDSinfo, 2016). Figure 3
shows the various stages of the HIV life cycle. Upon entry into the human body, the
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single-stranded RNA-HIV (founder virus) docks and infiltrates into the target cell. The
main target cells are the CD4+ T lymphocytes and other cells with the beta-chemokine
receptor (CCRS) or the alpha-chemokine receptor (CXCR4) (Coakley et al., 2005) and
these include resting CD4+ T-cells, monocytes and macrophages and dendritic cells
(Maartens et al., 2014). The binding of the virus to the target cells is an interaction
between the virion envelope glycoprotein (gp 120) with the chemokine co-receptors.
The infiltrated viral RNA is then reverse-transcribed into a double-stranded DNA by a
virally encoded reverse transcriptase, which was also transported into the host cell
during the infiltration process. Although the virally encoded reverse transcriptase has
multi-functional activity including RNA-dependent DNA polymerase activity, DNA-
dependent DNA polymerase activity and RNase-H activity, it has no proof-reading
activity lead, hence is prone to transcription errors (Hu and Hughes, 2012). The viral
double-stranded DNA then enters the host's cell nucleus and integrates into the host's
cellular DNA by viral encoded integrase and other host's co-factors (Smith and Daniel,
2006). Once integrated into the host's DNA, the virion takes over the cells cellular
machinery and dictates its replication activity. At this stage, two pathways are possible
for the virus;
a. The virus becomes latent and this allows it to avoid detection together with
its host cell from the host's immune system.
b. Alternatively, the virus may become active and is transcribed via messenger
RNA (mRNA) encoding for viral proteins and the assembling of the Gag
and Gag-Pol multi-protein complexes and translated into infectious viral
RNA which bud off the cell membrane surface into the extracellular fluid
or the blood stream to infect other cells (Pier, 2004; Reyskens et al., 2014).
HIV spreads between CD4+ T-cells by a hybrid spreading mechanism by
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either cell-free spread or cell-to-cell spread (Zhang et aI., 2015). This hybrid
spreading mechanism of HIV is considered to a source of the virion's
continual replication and resistance to ARVs (Sigal et al., 2011; Zhang et
aI., 2015).
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Key to Terms
HIV capsid: HIV's bullet-shaped
core that contains HIV RNA
HIV envelope: Outer surface of HIV
HIVenzvmes: Proteins that carry
out steps in the HIV life cycle
HIV glycoprotelns: Protein "spikes"
embedded in the HIV envelope
HIV RNA: HIV's genetic material
HIV Glycoprotelns
Figure 2. Schematic diagram of the human immunodeficiency virus (HN). Source
(AIDSinfo, 2016)
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The HIV Life Cycle
H'V med~c.lnc~ In Sill.drug CidSW5stoo e HIV at dlf e.tl~n(st~ge~ In tl'\e HIV IIle cvcte,
o Binding ("'0 c.ned Attlchrnent): HIV bind'
(~!t.Ctl<>1 ,t II) to' 001)10', on Iha .",toco ot. CD4 CQIi
Rev,n. Tnlnac,lptton: In"de Ihc CD4 c I, filV ,.IM.es and us.,
'~Cf!!e tt nscriptllSoe(en HIV enzyme) to convt}rt Its oenotlc
01.1 II 1-ljlV RNA-Into IoIIVDNA.T. he <o"".",on 01 HIV RNA to ~IIVDNA
=....Noft·_.....kM,_."..Iranscrlpta .. Inhlblto" (NNRTII)"anscrlpta .. ""'"'1torI (NAT")
R,pllcatlon: Once Intcgr"tc,olnto Ihc CD4 coli DNI\.HIV !J<l91f1. 10
u,.~ uie m.."chinory or th CD4 CC1~to1 f'1'\t'tk 101')9ch(llnJ of HJV creecins.
Tho orctem ch:'ltns ~,,, tho building blocks fo' mo,~ HIV
,ft 1)\ •• mtcgt ~o(lin MIV
I)nrymo) HIV u,~, Int!) M u
10 m Q,t ,C.,n,,to.gr.'.u» It:. v111)1DNA InlO DNA 0 111
(04 coil
e Int.gral. Inhlblton
HIV felease! prOfcue (lin HIV e-nzVmo). Protease acts to
break uo the long prohHn (,halns that fOIM the Immluvre
v.,u~. The smallet HIV proteins combine to ro,m mature
(In' euces) HIV,
o Prot .... Inhibitors ,'10)
Figure 3. HIV lifecycle and antiretroviral drug targets. Source: (AIDSinfo, 2016).
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Soon after infection, there is an intense viral replication and infection of lymphoid
tissue to establish the grounds for the virus (Moir et al., 2011). During this early stage
after infection, there is an intense production of inflammatory cytokine and chemokines
(Maartens et al., 2014). Revealing is that, this intense inflammatory processes during
the first stage is in sharp contrast to the more modest and delayed response as seen in
the activity of other viral infections (Stacey et al., 2009). In response to the acute
infection, HIV -specific CD8+ cells then kill infected cells leading to a decrease in viral
load but invariably selecting for the emergence of mutations to escape the immune
system response (Goonetilleke et al., 2009). Thus, in most individuals the acute state
leads to a profound exhaustion of HIV -specific T-cells programmed by the expression
of programmed death 1 (PD-l) leading to a loss of effector function (Trautmann et aI.,
2006).
About three months after the infection, neutralising antibodies appear which select for
viral escape mutants. This innate immune response to control the viral population
mediated through the killer cells however leads to the evolution of mutant cells to
counter the effect of these natural killer cells (Alter et al., 2011).
The most important characteristic feature of HIV infection is the combined reduced
production of CD4+ T-cells and their continual destruction together with their subtypes
like T-helper-17 cells and mucosal associated invariant T-cells needed for bacterial
defence (Prendergast et al., 2010; Cosgrove et aI., 2013). This combined effect on the
T-cells leads to a total depletion of their population (Prendergast et al., 2010; Cosgrove
et al., 2013; Maartens et al., 2014).
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2.2.6 Stages of HIV infection
After introduction of HIV into a host, three different stages of the infection is
characterised; an acute stage, a chronic stage and an advanced stage. The acute stage of
HIV disease is immediate after the infection of the virus and most infected individuals
experience non-specific flu-like symptoms like fever, sore throat, headache, nausea,
gastrointestinal disturbances and lor genital or mouth ulcers (Kahn and Walker, 1998;
DHHS, 2010).
The second stage of the infection is termed the clinical latency stage, asymptomatic
HIV or chronic HIV. Although most infected individuals experience fewer or no
symptoms at the early part of this stage, symptoms like weight loss, myalgia, fever and
persistent generalised lymphadenopathy may appear during the latter parts of this
second stage (DHHS, 2010). In the absence of ART, the second stage can last between
3 and 20 years.
The last stage of HIV disease is AIDS which is define by specific parameters of CD4+
count of <200 cells/uL and other symptomatic features like pneumocystis pneumonia,
cachexia and respiratory tract infections (DHHS, 2010). Cancers like Kaposi's sarcoma
and cervical cancer may occur in addition to the symptoms experienced during the first
stage of the infection.
2.2.7 HIV infection classification
Due to the chronic and progressive nature of HIV infection, HIV and HIV -related
diseases are clinically classified for appropriate management. There are basically two
different but related classification systems; the WHO HIV staging system which is
more appropriate for resource-limited setting and the CDC classification which on the
other hand is idle for developed countries.
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The WHO HIV staging classifies the course of the infection into five different stages
as follows:
• Primary HIV infection: An infected individual may be either symptomatic
or will exhibit acute retroviral syndrome.
• Stage I: An infected individual may be asymptomatic but with a CD4+ T-
cell count of <500 cells/ul.
• Stage II: An infected individual may show rmnor mococutaneous
manifestation and recurrent upper respiratory tract infection with a CD4+
T-cell count of <500 cells/ul.
• Stage III: An infected individual may show advanced symptoms like
chronic diarrhoea, tuberculosis of the lungs and a CD4+ T-cell count of
<350 cells/J.lL
• Stage IV: An infected individual in WHO stage IV is said to have AIDS.
Symptoms include toxoplasmosis of the brain, candidiasis of the
oesophagus and other organs and a CD4+ T-cell count of <200 cells/uL of
blood.
The CDC system classifies HIV infection into 5 stages as follows:
• Stage 0: This stage refers to the period immediate to < 180 days before an
infected person tests positive to the HIV test.
• Stage I: The infected individual shows no AIDS defining illness and a
CD4+ T-cell count of~500 cells/ul.,
• Stage 2: The infected individual shows AIDS defining illness and a CD4+
T-cell count of between 200 cells/ul. and 500 cells/ul.,
• Stage 3: The infected individual shows AIDS defining illness and a CD4+
T-cell count of s 200 cells/ J.lL.
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• Unknown: This stage refers to a known infected individual but without
sufficient clinical information to be classified to any of the above stages.
2.2.8 Therapeutic management of HIV infection
The development of ART in the mid 1990's has turned HIV infection into a chronic
infectious disease which when well managed can lead to a sustained life. Currently
more than 25 licensed drugs (approved by the U.S FDA) are available in managing HIV
infection (Maartens et al., 2014). These drugs effect their antiretroviral activity at
different stages of the HIV replication life-cycle (Figure 3). ARVs are generally
classified into 5 classes depending on the stage of the HIV life cycle that they interfere
with (Arts and Hazuda, 2012) as follows;
1. Fusion inhibitors/Entry inhibitors: This class of ARVs interfere with the
fusion and entry of the HIV virus into the target host cell. The fusion
inhibitors prevent the formation of the "hairpin" structure required for the
fusion of the virion to the host cell's cell membrane by attaching to the HIV
envelope glycoprotein gp41. The entry inhibitors are CCR5 co-receptor
antagonists and they block proteins on the CD4+ T-cells that are needed for
the virus to enter the target cell.
2. Nucleoside and nucleotide reverse transcriptase inhibitors
(NRTIs/NtRTIs): ARVs that belong to this class targets the virion's reverse
transcriptase, the enzyme employed by the virus during the reverse
transcription of viral RNA genome into genomic RNA. NRTlslNtRTIs thus
act as DNA-chain terminators during the virion's reverse transcription
process.
3. Non-nucleoside reverse transcriptase inhibitors (NNRTIs): This class
of ARV s also inhibit the virion's reverse transcriptase enzyme by binding
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to a spot close to the enzyme's active site (allosteric site) and thereby
causing a conformational change in the active site (non-competitive
inhibition) (Tantillo et aI., 1994; Spence et aI., 1995).
4. Integrase strand transfer inhibitors (INSTIs): Integrase inhibitors block
the integration of the viral double-stranded DNA into the host's DNA by
inhibiting the viral enzyme integrase (Arts and Hazuda, 2012).
5. Protease inhibitors (PIs): Protease inhibitors interfere with the activity of
HIV replication by inhibiting viral protease needed for the assembling of
the imler core (Gag and Gag-Pol multiprotein complexes) of viral proteins
before they bud off the cell surface to be matured virus (Hartman and
Buckheit, 2012).
Table 1 shows the various ARVs approved by u.s Food and Drugs
Administration and their therapeutic classes (AIDSinfo, 2016).
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Table 1. Anti-retrovirals and their therapeutic classes
Therapeutic Class Generic Name
Fusion Inhibitors Enfuvirtide
Entry Inhibitors Maraviroc
NucleosidelNucleotide reverse Abacavir
transcriptase inhibitors (NRTIsINtRTIs) Didanosine
Emtricitabine
Lamivudine
Stavudine
Tenofovir disoproxil fumarate
Zidovudine
Non-nucleoside reverse transcriptase Delavirdine
inhibitors (NNRTIs) Efavirenz
Etravirine
Nevirapine
Rilpivirine
Integrase Inhibitors Dolutegravir
Elvitegravir
Raltegravir
Protease inhibitors (PIs) Atazanavir
Darunavir
Fosamprenavir
Indinavir
Nelfinavir
Ritonavir
Saq uina vir
Tipranavir
Source: (AIDSinfo, 2016)
In addition to these classes of ARVs, a pharmacokinetic enhancer, cobicistat which
increases the effectiveness of ARVs have also been approved by the u.s FDA
(AIDSinfo, 2016). Also available are combination HIV medicines, which contain two
or more ARVs, from one or more drug classes (Table 2).
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Table 2. Combination anti-retroviral medicines
Generic Name
Abacavir and lamivudine
Abacavir, lamivudine, and zidovudine
Atazanavir and cobicistat
Darunavir and cobicistat
Efavirenz, emtricitabine, and tenofovir disoproxil fumarate
Elvitegravir, cobicistat, emtricitabine, and tenofovir alafenamide fumarate
Elvitegravir, cobicistat, emtricitabine, and tenofovir disoproxil fumarate
Emtricitabine, rilpivirine, and tenofovir alafenamide
Emtricitabine, rilpivirine, and tenofovir disoproxil fumarate
Emtricitabine and tenofovir alafenamide
Emtricitabine and tenofovir disoproxil fumarate
Lamivudine and zidovudine
Lopinavir and ritonavir
Abacavir, lamivudine, and zidovudine
Source: (AIDSinfo, 2016)
The management of HIV involves the continuous monitoring of immunological,
virological and clinical indices in PLHIV . ART requires the use of ARV s to change the
natural history of HIV infection by preventing clinical progression. The development
of effective combination ART in the mid-1990s led to the rapid, widespread clinical
use in developed countries. This resulted in reducing the death of PLHIV by two-thirds
from 1995 to 1997 (Mouton et al., 1997; Palella et al., 1998). Since then ART regimens
have become easier to take, less toxic, and more potent (Gallant et al., 2008; Mills et
al., 2009; Molina et al., 2010). In addition, the average life expectancy after HIV
diagnosis of a HIV -positive patient who is treated appropriately with ART increased
from 10.5 years in 1996 to 22.5 years in 2005 (Harrison et al., 2010) and now is
estimated to approach that of the general population (Bhaskaran et al., 2008). The use
of ART in managing HIV infection leads to a suppression of viral replication, an
improvement in CD4+ T-cells count and an improvement in the function of the general
immune system.
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In the developing countries the use of ART expanded markedly after year 2000 with
demonstrated clinical benefits similar to those seen in developed countries. It is
currently estimated that 5 million PLHIV in the developing countries are on ART
(WHO, 20 13c). Despite these marked improvements, challenges of access, adherence,
toxicity, drug-drug interactions, and drug resistance remain, particularly in
disadvantaged populations (Gulick, 2010). The current WHO recommendations
indicates that as a priority, ART should be initiated in all HIV -positive individuals
(adults and adolescents) with CD4 count between 350 cells/ul. and 500 cells/ul.
regardless of WHO clinical stage (strong recommendation, moderate-quality evidence)
(WHO,2013c).
Current therapy involves the combination of drugs from three different classes;
NRTIINtRTI, NNRTI and boosted-PI. The WHO Guidelines currently recommend as
a first line therapy for HIV infection, dual NRTIINtRTI combinations of either
tenofovir (TDF) and lamivudine (TC) (or emtricitabine-FTC), in combination with a
single NNRTI, efavirenz (EFV) as a single pill, given once daily (strong
recommendation, moderate-quality evidence) (WHO, 2013c). However, in situations
where TDF + 3TC (or FTC) + EFV is contraindicated or not available, one of the
following options is recommended (strong recommendation, moderate-quality
evidence) (WHO, 2013c).
• Zidovudine (AZT) + Lamivudine (3TC) + Efavirenz (EFV)
• Zidovudine (AZT) + Lamivudine (3TC) + Nevirapine (NVP)
• Tenofovir (TDF) + Lamivudine (3TC) (or Emtricitabine-FTC) +
Nevirapine (NVP)
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2.2.9 HIV/AIDS epidemiology in Ghana
HIV was first identified in Ghana in March 1986 and since then the epidemic has spread
slowly. In 1987, the National AIDS/STI Control Programme (NACP) was established
under the Ministry of Health to implement and coordinate the country's HIV/AIDS
programme through various strategies. These strategies include maintaining a safe
blood supply, ensuring safe use of needles, and disseminating information through
public campaigns to change social attitudes and behaviour to contain and limit the
spread of the infection. In 2000, the Ghana AIDS Commission (GAC) was established
for effective resource mobilization, management, and co-ordination of HIV IAIDS
activities being conducted by various organisations. Since then the NACP and the GAC
have concerted their efforts to reduce the spread of HIV in Ghana in addition to
formulating policies on HIV -care in our various hospitals.
The HIV epidemic in Ghana is described as a generalised epidemic with a consistent
prevalence of more than 1% in the general population (GHS and NACP, 2012; GSS et
al., 2015). Prevalence estimates of HIV in Ghana has mainly been derived from HIV
sentinel surveillance (HSS) that focuses on HIV testing of pregnant women who attend
antenatal clinics. This data (generated yearly since 1992) is generated from results of
conducting HIV test for pregnant women seeking antenatal care for the first time and
patients newly diagnosed with sexually transmitted infections attending STI clinics in
the sentinel sites. The data is collected using an anonymous, unlinked method, and the
results reported yearly by the NACP. Using the annual HSS the HIV epidemic in Ghana
appears to be in a downward trend between 2003 and 2013, from 3.6% in 2003 through
to 2.2% in 2008 and to 2.0% in 2011 (NACP, 2008; NACP, 2009; NACP, 2010). Data
from the 2012 and 2013 HIV sentinel survey indicate HIV prevalence of l.37% and
1.30% respectively (NACP, 2014). This shows a downward trend in the prevalence of
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the disease between 2003 and 2013. However, data from the 2014, 2015 and 2016
sentinel reports suggest an upsurge of HIV prevalence. The prevalence of HIV in 2014
was 1.6% (up from 1.3% in 2013) and further surged to 1.8% in 2015 and in 2016 a
third consecutive upsurge of the prevalence to 2.4% (NACP, 2015; NACP, 2016;
NACP, 2017).
Although data from pregnant women in HSS has been shown to be a reasonable proxy
for HIV prevalence estimation (WHO and UNAIDS, 2000) some limitations are present
when using sentinel surveillance estimates derived exclusively from pregnant women
attending select antenatal clinics for estimating the HIV rate in the general adult
population. These limitations include the fact that the HSS does not capture information
on HIV prevalence in men, sexually inactive women, women who protect themselves
from HIV and pregnancy using condoms, non-pregnant women, nor in women who
either do not attend a clinic for pregnancy care or receive antenatal care at facilities not
represented in the surveillance system. Despite these limitations, HSS data is very
useful for monitoring trends in HIV levels. In 2003, the Ghana Demographic and Health
Survey (GDHS) introduced an instrument to measure the sero-prevalence of HIV in the
country. The GDHS data which is a population-based data is intended to improve the
calibration of annual HSS data, so that trends in HIV infection can be more accurately
measured in the intervals between general population surveys (WHO and UNAIDS,
2000). Two of such population-based HIV sero-prevalence is currently available in the
2003 and 2014 GDHS. The 2003 GDHS indicated HIV prevalence (in 15-49 years old)
of 2.2% with the 35-39 years' age group having the highest prevalence of 3.5%. In
addition, the Eastern region recorded the highest HIV prevalence of 3.7% and Greater
Accra recorded a prevalence of 2.2% (95% CI, 1.8-2.5) (GSS et al., 2004). Data from
the 2014 GDHS showed a slightly lower but insignificant decrease in HIV prevalence
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(in 15-49 years old) to 2.0% (95% CI, l.6-2.4) and the 40-49 years' age group having
the highest prevalence of 3.8% (GSS et aI., 2004). The Greater Accra prevalence
increased to 3.8%, whilst the highest prevalence of 4.5% was recorded in Eastern
Region (GSS et aI., 2004),
Although the HSS data indicate a rather gently but continual upsurge in prevalence of
HIV in Ghana between 2014 and 2016, the availability of population-based sero-
prevalence data from the 2003 and 2014 GDHS obviously enhances the body of
information available on the HIV IAIDS epidemic in Ghana.
2.2.10 Anti-retroviral therapy and HIV management in Ghana
Ghana, in order to sustain the progress been made in the area of care for PLHIV and to
strive for the best impact of reducing morbidity among PLHIV whilst ensuring a
reduction in the incidence of new infection has adopted the 2013 WHO guidelines
resulting in the necessity of reviewing its guidelines for ART. The current 5th edition
guidelines recommends two (2) NRTIs/NtRTI + one (1) NNRTI or two (2) NRTIs +
one (1) boosted PI (NACP et al., 2014). In Ghana, the current drugs used are shown in
Table 3 (NACP et al., 2014).
Table 3. Drug regimens used in managing HIV infection in Ghana
Therapeutic Class Drug Abbreviation
NRTI Zidovudine AZT/ZDV
Lamivudine 3TC
Emtricitabine FTC
NtRTI Tenofovir TDF
NNRTI Nevirapine NVP
Efavirenz EFV
Boosted PI Ritonavir boosted Atazanavir ATV Ir
Ritonavir boosted Lopinavir LPV Ir
NRTI=Nucleoside reverse transcriptase inhibitor; NtRI=Nucleotide reverse
transcriptase inhibitor; NNRTI=Non-nucleoside reverse transcriptase inhibitor;
PI=Protease inhibitor
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The first line drugs are
• TDF + 3TC (or FTC) + EFV- Preferred Regimen or
• TDF + 3TC (or FTC) + NVP-Alternative regimen or
• AZT + 3TC + NVP- Alternative regimen or
• AZT + 3TC + EFV- Alternative regimen
The second line drugs are
• AZT + 3TC (or FTC) + LPV/r (or ATV/r)- l " Alternative Regimen or
• TDF + 3TC (or FTC) + LPV/r (or ATV/r)- 2nd Alternative Regimen
These guidelines are based on several factors including efficacy, convenience, toxicity,
drug resistance and especially for resource-limited settings, accessibility, availability
and cost. In consideration of these guidelines, ART must still be individualised to the
patient, which requires a careful assessment of each patient's conditions and ART
readiness.
2.3 Cardiovascular diseases, hypertension, HIV/AIDS and ART
Non-communicable diseases (NCDs), which are among the known chronic diseases,
are of long duration and generally slow progression. They share common risk factors
mainly tobacco use, unhealthy diets (high in fat, salt, and refined sugars), physical
inactivity and harmful use of alcohol. Worldwide, NCDs are the leading causes of death
accounting for 63% of the 57 million deaths yearly, with 80% of these deaths occurring
in developing countries and one-third occurring in people below 60 years (WHO,
20lla). The four major non-communicable diseases namely cardiovascular diseases
(CVDs), cancers, chronic respiratory diseases and diabetes account for about 80% of
the total NCD deaths (Nigatu et al., 2012). NCDs are among the co-morbidities that
exit with HIV/AIDS (Hirschhorn et al., 2012) and co-morbidities are becoming more
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important in the comprehensive management of HIV in PLHIV (Meguid El Nahas and
Bello, 2005). NCD co-morbidity in PLHIV has gained attention due to the fast
emergence ofNCDs as a major public health disease importance in high HIV prevalent
areas especially in low-and-middle income countries where the incidence of NCDs is
on the rise (IDF et al., 2009). Co-morbidity refers to the occurrence of a disease(s) or
disorder together with an index disease in an individual. The measurement of co-
morbidity as a confounder, an effect modifier, an exposure or an outcome m
epidemiological studies has recently received more attention due to the issue of
comprehensi ve healthcare of patients.
2.3.1 Cardiovascular diseases in PLHIV
Cardiovascular diseases (CVDs), one of the major NCDs, is a term used commonly to
refer to a group of disorders of the heart and blood vessels which include coronary heart
disease (such as angina and myocardial infarction), cerebrovascular disease, peripheral
arterial disease, rheumatic heart disease, congenital heart disease, deep vein thrombosis
and pulmonary embolism (WHO, 2016). CVDs are the number one cause of death
globally with projections indicating mortality due to CVDs will reach 23.9 million by
the year 2030 (Mathers and Loncar, 2006). CVDs in LMIC is reported to represents
more than 80% of the global burden ofCVDs (Yusufet al., 2004).
With the introduction of ART in the mid-1990s, there has been a great reduction in
morbidity and mortality attributed to HIV /AIDS in PLHIV. This has led to an
improvement in the quality of life of HIV -positive patients and an increase in life
expectancy (WHO, 2009a). However, this is being challenged by the occurrence of
CVDs and metabolic syndrome in PLHIV (Bain et al., 2016; Benjamin et al., 2016).
Studies from HIC with large sample sizes have implicated HIV and ART as likely
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mediators of this observed increase in the risk of CVDs in PLHIV and reports have
cited CVDs as a significant cause of morbidity and mortality in PLHIV (Mary-Krause
et al., 2003; Friis-Moller et al., 2007; Duprez et al., 2009). An analysis of 13 cohort
studies in HIC implicated CVDs as the most common non-AIDS related cause of death
among HIV -positive patients (Colloboration, 2010). A multi-centred, comparative,
cross-sectional survey of HIV -infected individuals in 12 countries on 5 continents
reported in 2014 indicated a prevalence of self-reported CVD risk factors in PLHIV of
28% and in PLHIV in SSA to be 12% (Sherer et al., 2014). Although HIV itself is a
chronic inflammatory condition, the causes of these co-morbid abnormalities are
complex as the association between HIV and CVDs is not fully understood.
Nonetheless, it is established that it involves phenomena related to the virus, ARVs
administered and to the host (Chastain et al., 2015).
Whilst some studies indicate that certain ARVs such as PIs may elevate the risk of
CVDs by raising the level of cholesterol and triglycerides in the blood (Malvestutto and
Aberg, 2010), other studies contradict this (Bavinger et al., 2013). The institution of
ART may place HIV -infected individuals at a greater risk of cardiovascular diseases
due to the effect some ARVs on lipid metabolism and mitochondrial function (Grunfeld
et al., 2008). A study in the USA where HIV -positive patients were compared with
HIV -negative patients indicated an association between HIV and CVD risk after taking
into account Framingham risk factors, co-infections and substance abuse (HR = 1.48;
95% [CI, 1.27-1.72]) (Freiberg et al., 2013).
A systematic review and meta-analysis conducted by Islam et al. (2012), involving 23
studies indicated that, the relative risk ofCVD in PLHIV on ART compared with HIV-
uninfected people was 2.00 (95% CI, 1.70-2.37, p<O.OOl), whilst the relative risk of
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CVD in PLHIV but ART-naive compared with HIV-uninfected people was 1.61(95%
CI, 1.43-1.81, p<O.OOl). The review also reported an association between ART and risk
of CVD (RR=1.52, [95% CI, 1.35-1.70], p=O.OOl) and also reported an association
between PI-based therapy and CVD (Islam et al., 2012).
Another systematic review of the risk of cardiovascular disease in PLHIV on ART
conducted by Bavinger et al. (2013) indicated that there is an association between
cumulati ve and recent exposure to ART and the risk of myocardial infarction (MI), a
CVD. The ARVs implicated in the review were recent exposure to abacavir and PIs
and cumulative exposure to indinavir and lopinavir (Bavinger et al., 2013). This was at
the background that some earlier studies have associated cardiovascular disease with
ART in general and specific ARVs, abacavir and didanosine (Friis-Moller et al., 2003;
Friis-Moller et al., 2007; Sabin et al., 2008; Worm et al., 2010) whilst others have
reported contrasting evidence of no association (Brothers et al., 2009; Lang et al., 2010;
Ribaudo et al., 2011; Ding et al., 2012).
These evidences thus suggest an increased risk of CVD among PLHIV with ARVs
being implicated in this increased risk of CVDs in PLHIV.
2.3.2 Cardiovascular diseases in PLHIV in sub-Saharan Africa
In the midst of an epidemiological transition from infectious diseases to non-
communicable diseases, the prevalence of CVDs have increased in most countries in
SSA. However, mortality due to CVD is reported to have decreased in SSA over the
past few decades (Stringhini et al., 2012; Roth and Murray, 2013) despite earlier
projections of an increase in CVD attributable deaths to 2.4 million by 2030 (WHO,
2004). This can be attributed to much attention in the diagnosis, treatment, and
prevention of CVDs (Joshi et al., 2008; Mayosi et a1., 2009; Chin, 2012; Chow et al.,
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2013) including appropriate and cost effective intervention methods (Lim et al., 2007;
Ortegon et aI., 2012) in the general population in SSA.
Most countries in SSA have also deployed the use of ART. According to a WHO report
in 2011, 49% of all eligible HIV -positive patients in SSA were receiving ART as at
2010. This has greatly affected the life of PLHIV in Africa. However, coincidentally
and parallel to the ART era in SSA, is the epidemiological transition ofNCDs including
CVDs been experienced by most SSA countries. Although relatively few studies are
available in the co-morbid condition of CVDs and HIV /AIDS in PLHIV in SSA, there
has been reviews of studies in the occurrence of cardio-metabo1ic traits and CVDs in
PLHIV in SSA (Mutimura et aI., 2008; Levitt et al., 2011; Dillon et al., 2013; Syed and
Sani, 2013; Bloomfield et al., 2014a; Nguyen et al., 2016; Hyle et al., 2017; Mayne and
George, 2017). A systematic review and meta-analysis of 49 published and 3
unpublished studies involving 29,755 individuals in SSA suggested differences in
cardio-metabolic traits between HIV -infected and uninfected individuals in SSA with
HIV infection being associated with TGs, HDL, BMI, sBP and dBP (Dillon et al.,
2013). The review also reported of ART use associated with LDL, HDL and
haemoglobin Alc (HbA1c) (Dillon et al., 2013). Another review of published literature
in LMIC indicated that CVDs like heart failure, hypertension, coronary heart disease
and stroke is common and more frequent in PLHIV compared with the general
population (Bloomfield et al., 2014a).
Stroke is a known CVD of which prevalence is increasing in most countries in SSA and
is associated with high rates of mortality, morbidity, and post-stroke disability (Kolapo
and Vento, 2011; Chin, 2012; Norrving and Kissela, 2013). Similar to studies
conducted in HIC which have associated stroke with HIV infection (Ovbiagele and
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Nath, 2011; Rasmussen et aI., 2011; Chow et aI., 2012), data emerging from SSA also
suggest a high prevalence of stroke among PLHIV. Whilst some studies in South Africa
have indicated no significant overall increased rate of stroke in association with HIV
infection, other studies indicated otherwise (Hoffmann et aI., 2000; Mochan et al., 2003;
Patel et aI., 2005). However, the prevalence of cryptogenic stroke has been reported to
be more common among HIV -infected persons compared with age- and sex-matched
HIV -seronegative controls suggesting the presence of a co-existent pro-thrombotic
state in HIV infection (Hoffmann et aI., 2000). Other studies also found HIV -positive
stroke patients to be of younger age and had fewer traditional CVD risk factors
compared with HIV -uninfected stroke patients (Hoffmann et aI., 2000; Kumwenda et
aI., 2005; Tipping et aI., 2007; Heikinheimo et aI., 2012) indicating a role of HIV
infection and non-traditional risk factors in the aetiology of stroke in PLHIV including
the role played by immunosuppression and opportunistic infections.
Although data from SSA on the implication of HIV-infection on heart failure in PLHIV
were mostly generated in the era when ART was not widely available in SSA, the
prevalence of heart failure in PLHIV was reported to be between 9% and 57% (Hakim
et aI., 1996; Longo-Mbenza et al., 1997; Longo-Mbenza et aI., 1998; Niakara et al.,
2002; Ntsekhe and Hakim, 2005). Studies have also implicated cardiomyopathy as a
relevant form of HIV associated CVD with prevalence between 5% and 29% in PLHIV
in SSA (Tantchou Tchoumi et aI., 2011; Chillo et al., 2012; Sliwa et al., 2012; Syed
and Sani, 2013) and often associated with low CD4 counts, higher viral load, and
advanced HIV stage in young persons with HIV infection (Nzuobontane et aI., 2002;
Twagirumukiza et aI., 2007). Although specific ART drug classes have been associated
with a higher prevalence of cardiomyopathy in the post-ART era (Tanuma et aI., 2003),
the relationship between peripartum cardiomyopathy and HIV remains unclear in
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PLHIV in SSA (Longenecker et aI., 2011; Sliwa et aI., 2011). Studies in PLHIV in SSA
have also reported of the presence of large thrombus burden rather than having
substantial atherosclerotic plaques in HIV -positive individuals suggesting a unique
pathophysiology that may be related to thrombophilia (Becker et aI., 2010; Becker et
aI.,2011)
Whilst some studies in SSA have reported of no evidence of increased atherosclerosis
in PLHIV in SSA (Lazar et aI., 2009; Fourie et al., 20] 5) most other studies have
reported of early subclinical atherosclerosis in PLHIV in SSA when compared with
Hlv-uninfected study participants (Fourie et aI., 2011; Ngatchou et aI., 2013;
Ssinabulya et al., 2014; Siedner et al., 20]6; Feinstein et al., 2017). A study in Uganda
by Ssinabulya et al. (2014), reported of a prevalence of 18% of sub-clinical
atherosclerosis in PLHIV (Ssinabulya et al., 2014). Two studies in Uganda reported of
subclinical atherosclerosis and arterial stiffness in HIV -positive patients attending
clinic with an even higher risk among patient with older age, elevated BMI, elevated
LDL-cholesterol and ART-exposure (Ssinabulya et aI., 2014; Siedner et al., 2016). A
study conducted among South Africans indicated a prevalence of 12% of subclinical
atherosclerosis in PLHIV but indicated no association with HIV -related factors
(Schoffelen et al., 2015). Another study in conducted among South Africans also
indicated that among a cohort of 255 patients undergoing vascular surgery, 32% were
HIV -positive with 23% being ART-exposed. However, no association was found
between HIV-infection and myocardial infarction (Redman et aI., 2014).
With the fact that hypertension is counted as an important risk factor for myocardial
infarction (a CVD) among Africans (Steyn et al., 2005), it is important to determine the
factors associated with hypertension among PLHIV especially in resource-constrained
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countries. Despite the high volume of studies on CVDs in PLHIV in HIC, it would be
inappropriate to extend conclusions from studies in HIC to SSA as there are differences
in terms of socio-demographic, economic and related characteristics (Edward et al.,
2013). A study conducted in South Africa on PLHIV on ART indicated that using the
D:A:D risk equation, 31.1% of participants had a moderate to high 5-year D:A:D
cardiovascular risk but a 6.7% of a moderate to high 1O-year Framingham risk of CVD
(Mashinya et al., 2015).
2.3.3 Hypertension as a cardiovascular risk factor in PLHIV
Hypertension, one of the five risk factors for metabolic syndrome, is a major factor used
in assessing the risk of CVDs. Blood pressure levels are continuous and consistent with
the risk of CVD events and it is independent of other risk factors such that the higher
the blood pressure levels, the greater the chances of heart attack, stroke, heart failure
and renal disorder (Chobanian et al., 2003). This risk has been estimated to be such
that for every 20 mmHg increment in systolic blood pressure or 10 mmHg increment
in diastolic blood pressure (115175 to 185/115 mmHg), the risk of CVD doubles
(Lewington et al., 2002) and this also doubles the risk of mortality from ischemic heart
disease and stroke (Chobanian et al., 2003). Worldwide, hypertension is reported to be
the leading risk factor for cardiovascular and cerebrovascular mortality and the leading
global risk for mortality (responsible for 13% of deaths globally) (WHO, 2009b).
A wide range of hypertension prevalence has been reported globally in PLHIV from
different studies. However, in most of these studies, the conventional risk factors
associated with this prevalence were the same as the general population such as age,
male sex, overweight, and hyper-triglyceridemia (Arruda Junior et al., 2010). Two
studies conducted in Brazil showed a prevalence of hypertension in PLHIV of 19.9%
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(Silva et al., 2009) and 25.6% (Arruda Junior et al., 2010). A multi-centre study in
South America indicated a prevalence of 31.5% using criteria of hypertension of sBP
2:130mmHg and/or dBP 2:85mmHg (Calm et al., 2010). Studies conducted in Norway,
Spain, Italy and the United States indicated a prevalence of hypertension between 20%
and 40% among populations exposed to ARVs (Bergersen et al., 2003; Gazzaruso et
al., 2003; Palacios et al., 2006; Baekken et al., 2008; Coloma Conde et al., 2008; Mothe
et al., 2009). This therefore calls for the need to examine the association between
HIV/AIDS, ART and CVDs and its risk factors including hypertension in order to be
informed on this double burden of disease on both mortality and morbidity in PLHIV.
2.3.4 Prevalence of hypertension in PLHIV in sub-Saharan Africa
Most countries in SSA are experiencing an ageing ofthe population, which in addition
to the epidemiological transition has resulted in an increase in the prevalence of
hypertension. This rise in the prevalence of hypertension is expected to increase the
burden of diseases in PLHIV in SSA. A review of the literature published on studies
conducted in SSA indicates several prevalence of hypertension in PLHIV and
specifically in those on ART (ART-exposed) compared with those not on ART (ART-
naive). Among the general HIV -positive population, varying prevalence has been
reported in different countries. Two studies in Cameroon both reported prevalence of
hypertension of28.5% in PLHIV (Dimodi et al., 2014; Dimala et al., 2016) and a third
study reported a prevalence of 25.5% (Nsagha et aI., 2015). Studies in South Africa
reported a prevalence of 19.5% in PLHIV (Malaza et al., 2012); Uganda, 27.9%
(Mateen et aI., 2013), 8.0% (Sander et aI., 2015) and 11.0% (Kwarisiima et aI., 2016)
in PLHIV.
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A cross sectional study conducted in Tanzania reported a prevalence of hypertension of
26.2% in PLHIV and was significantly higher among those on ART compared with
patients yet to be on ART (Kagaruki et al., 2014). However, a multi-centred cross-
sectional study in Tanzania involving 12 HIV Care and Treatment Centres and a study
population of34,111 PLHIV reported a prevalence of 12.5% of hypertension in ART-
naive patients (Njelekela et al., 2016).
A study conducted in Kenya indicated an 11.2% prevalence of hypertension among
Kenyan HIV -positive men and 7.4% prevalence among Kenyan HIV -positive women
(Bloomfield et al., 2011). Overweight/obesity was also prevalent among men (10.6%)
but higher among women (22.6%). In Ghana, Ngala and Fianko (2014) reported a
systolic hypertension prevalence of 15.2% and a diastolic hypertension prevalence of
23.8% in ART-exposed HIV -positive patients whilst a systolic hypertension prevalence
of 3.5% and diastolic hypertension prevalence of 6.4% was reported in ART-naive
HIV -positive patients.
A study in Nigeria reported of a prevalence of hypertension of 16.3% in ART -exposed
patients and 9.0% in ART-naive patients (Nduka et al., 2016b). A study in Senegal
indicted that the prevalence of hypertension among PLHIV was 28.1 % (Diouf et al.,
2012). Table 4 below shows a summary of the various studies conducted in SSA
reporting on the prevalence of hypertension in PLHIV. These studies point to the fact
that although most of them were conducted as cross-sectional studies, there is a wide
variation in the prevalence reported between countries and even within some countries.
This calls for a systematic review and meta-analysis of the prevalence of hypertension
(and CVDs) in PLHIV in SSA.
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Table 4. Studies assessing prevalence of hypertension in PLHIV in sub-Saharan Africa.
Sample 0/0 Prevalence of hypertension in study participants (%)
Country Authors Year Study-design size female Overall ART -exposed ART-naive
Botswana Schwartz et al. 2011 Cross-sectional 179 57.5 14.0 NR NR
Shapiro et al. 2012 Cross-sectional 62 100 NR 65.0 28.0
Cameroon Ekali et al. 2013 Cross-sectional 143 72.0 NR 27.8 10.7
Dimodi et al. 2014 Cross-sectional 463 74.7 28.5 31.3 20.3
N sagha et al. 2015 Cross-sectional 215 74.9 25.6 29.4 14.5
Dimala et al. 2016 Cross-sectional 200 70.0 28.5 38.0 19.0
Ghana Ngala and Fianko 2014 Cross-sectional 305 68.2 25.6 SH: 15.2; DH: 23.8 SH: 3.5; DH: 6.4
Kenya Nyabera et al. 2011 Cross-sectional 4629 33.0 32.0 NR NR
Bloomfield et al. 2011 Cohort 12,194 64.8 Men: 11.2; NR NR
Women: 7.4
Bloomfield et al. 2014 Cohort 49,475 74.0 Men: 10.2; NR NR
Women: 7.0
Nigeria Denue et al. 2012 Cohort 268 48.9 NR NR 26.4
Edwards et al. 2013 Cross-sectional 265 67.5 NR 12.1 7.8
Muhammad et al. 2013 Cross-sectional 200 53.0 NR 17.0 2.0
Ogunmola et al. 2014 Cross-sectional 250 62.4 NR 12.3 19.0
Nduka et al. 2016 Cross-sectional 406 68.5 NR 16.3 9.0
Senegal Diouf et al. 2012 Cross-sectional 242 57.9 NR 28.1 NR
South Julius et al. 2011 Cross-sectional 304 78.0 NR 19.1 NR
Africa Malaza 2012 Cross-sectional 2513 71.4 19.5 NR NR
Mashinya et al. 2014 Cross-sectional 89 70.8 NR NR 42.7
Mashinya et al. 2015 Cross-sectional 214 79.9 NR 26.2 NR
Oni et al. 2015 Cross-sectional 5511 73.3 NR 19.7 NR
SH=Systolic hypertension; DH=Diastolic hypertension; PLHIV=Persons living with HIV; NR=Not reported
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Table 4 (cont.). Studies assessing prevalence of hypertension in PLHIV in sub-Saharan Africa.
Sample % Prevalence of hypertension in study participants (%)
Country Authors Year Study-design size female Overall ART -exposed ART -naive
Tanzania Perk et al. 2014 Cross-sectional 301 67.8 NR 28.7 5.3
Kagaruki et al. 2014 Cross-sectional 671 70.5 26.2 30.0 2l.9
Njelekela et al. 2016 Cross-sectional 34,111 67.2 NR NR 12.5
Uganda Scholten et al. 2011 Cross-sectional 189 55.0 NR Men: 3l.6; Men: 28.9;
Women: 25.3 Women: 12.2
Mateen et al. 2013 Cross-sectional 5,563 66.9 27.9 NR NR
Sander et al. 2015 Cross-sectional 426 71.0 8.0 8.2 7.8
Kwarisiima et al. 2016 Cross-sectional 3,545 65.0 1l.0 NR NR
Zimbabwe Mutede et al. 2015 Cross-sectional 393 56.7 NR 34.9 NR
SH=Systolic hypertension; DH=Diastolic hypertension; PLHIV=Persons living with HIV; NR=Not reported
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2.3.5 Review of factors associated with hypertension in PLHIV
Most studies determining the prevalence of hypertension in PLHIV have also reported
of the factors associated with it. In most of these studies, the reported associated factors
included the risk factors of hypertension as pertains in the general population; age,
elevated body mass index, diabetes, smoking, excessive alcohol use and
hyperlipidaemia. However, PLHIV present HIV -related factors like CD4+ T-cell count,
viral load and components of the ART (ARVs) which must also be investigated in order
to determine their association with the increasing prevalence of hypertension in PLHIV
in the ART era.
2.3.5.1 Socio-demographic and life-style factors
Several socio-demographic and life-style factors have been investigated to determine
their association with hypertension in PLHIV. These include age, sex, marital status,
education, employment status, alcohol use, smoking of tobacco, regular physical
activity, fruit and vegetable consumption and family history of cardiovascular event.
2.3.5.1.1 Age
Increasing age is one of the known risk factors associated with hypertension in the
general population. The risk of hypertension increases with increasing age such that
over 50% of persons between 60 and 69 years and three-quarters of those aged 70 years
are hypertensive (Burt et aI., 1995). This increased risk of hypertension as age increases
is present in all age groups 2: 40 years and is associated with the age-related rise in
systolic blood pressure (Chobanian et aI., 2003). Most studies have also associated
increasing age with the risk of hypertension in PLHIV (Crane et aI., 2006; Palacios et
aI., 2006; Baekken et aI., 2008; Medina-Tome et aI., 2012; Manner et aI., 2013b; De
Socio et aI., 2014). Two large cohort studies, the Data Collection on Adverse Events of
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Anti-HIV Drugs (D:A:D) (Friis-Moller et al., 2003) and the Multicentre AIDS Cohort
Study (MACS) (Seaberg et al., 2005) also indicated a positive association between age
and hypertension. A study reported by Arruda Junior et aI. (2010), indicated that the
odds of hypertension in PLHIV above the age of 40 years was 3.06 (adjusted OR=3.06
[95% CI, 1.91-4.97], p<O.OOl) compared with those :S40 years. Although most study
results are in this direction, Hajazi et al. (2013), reported of a lack of association
between PLHIV aged ~40 years and hypertension in Malaysia (adjusted OR=0.83 [95%
CI, 0.35-1.98], p=0.674) but an increased odds of hypertension for every additional age
in years (adjusted OR=1.07, [95%, CI, 1.02-1.12], p=0.010).
Studies conducted in SSA have also reported of an association between age and
hypertension. A large retrospective cohort study in Kenya reported of an association
between increasing age and the risk of hypertension in both men (OR=2.21 [95% CI,
1.95-2.50]) and women (OR=1.61 [95% CI, 1.40-1.87]) (Bloomfield et al., 2011). Other
studies have also reported an association between increasing age and hypertension in
PLHIV in SSA (Dioufet al., 2012; Okello et al., 2015; Sander et al., 2015; Kwarisiima
et al., 2016; Njelekela et al., 2016). Other studies have reported of an association
between PLHIV aged >40 years and hypertension (adjusted OR=2.29 [95% CI, 1.49-
.02], p=0.002) (Dimala et al., 2016). Such an association have also been reported in
patients >50 years compared with :S50years (Mashinya et al., 2015), patients >45 years
compared with :S45 years (Mutede et al., 2015) and in patients aged >40 years (both
ART-experienced and ART-naive) (Kagaruki et al., 2014).
2.3.5.1.2 Sex
Many studies have reported men being at a greater risk of CVDs and show higher blood
pressure levels when compared with their age-matched pre-menopausal women in the
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general population (Kusuma et al., 2002; Jervase et al., 2008). Although most studies
have determined the association between sex and hypertension in PLHIV, the results
have been conflicting. The MACs study reported of a strong association with males
having higher odds of hypertension compared with females (Friis-Moller et al., 2003;
Thiebaut et al., 2005). A similar association was reported with males having higher
odds of hypertension (adjusted OR=2.55 [95% CI, 1.23-5.29], p=0.012) (Manner et al.,
2013b). Other studies have also reported similar increased odds of hypertension in men
compared with women; (adjusted OR=1.85 [95% CI, 1.15-3.01], p=0.019) (Arruda
Junior et al., 2010), (adjusted OR=3.03 [95% CI, 1.18-.79], p=0.022) (Hejazi et al.,
2013 ) and (adjusted OR=1.63 [95% CI, 1.18-2.26]) (De Socio et al., 2014). However,
other studies have reported no association between sex and hypertension in PLHIV
(Jerico et al., 2005; Palacios et al., 2006; Medina-Tome et al., 2012; Ikeda et al., 2013).
Studies reported from SSA have also indicated conflicting results. Whilst some studies
indicate a significantly higher prevalence of hypertension in men compared with
women among PLHIV (Denue et al., 2012; Mashinya et al., 2015; Njelekela et al.,
2016), other studies indicate of no such difference or no association between sex and
hypertension in PLHIV (Edward et al., 2013). Dimala et al. (2016) and Mateen et al.
(2013) both indicated that men are at higher odds of hypertension compared with
women. Whilst a study by Mutede et al. (2015), reported of females of being at higher
odds of hypertension (adjusted OR=2.38 [95% CI, 1.31-4.34]), other studies have
reported of no association between sex and hypertension among PLHIV (Diouf et al.,
2012; Ogunmola et al., 2014; Kwarisiima et al., 2016).
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2.3.5.1.3 Marital status
Studies, which have determined the association between marital status and
hypertension, have reported of married individuals having elevated blood pressure than
the never married in the general population (Lipowicz et aI., 2002; Ekezie et aI., 2009).
However, a review of the literature indicates no such determined relationship in PLHIV.
2.3.5.1.4 Education and employment status
Few studies have determined the association between education and hypertension in
PLHIV. Whilst a study by Krauskopf et aI. (2013), associated college education with
lower odds of hypertension in PLHIV (adjusted OR=0.74 [95% CI, 0.57-0.98]), other
studies have reported of no association between highest educational level attained and
hypertension in PLHIV (Ikeda et aI., 2013; Ogunmola et aI., 2014; Kwarisiima et aI.,
2016).
In terms of employment, two studies both reported of lack of association between
employment status and hypertension (Krauskopf et aI., 2013; Hejazi et aI., 2013 ), but
a study reported by Mutede et aI. (2015), indicated a lower odds of hypertension in
PLHIV working in an informal sector (adjusted OR=0.49 [95% CI, 0.25-0.96]).
2.3.5.1.5 Alcohol use
Excessive alcohol use is associated with high blood pressure regardless of other risks
factors (Beilin et aI., 1996; Gravlee et aI., 2005; Nunez-Cordoba et aI., 2009; Fu et aI.,
2010) and alcoholic beverage reduction has been associated with lowering of both
systolic and diastolic blood pressure, with a dose-response relationship (Xin et aI.,
2001) in the general population. On the other hand, results from the 20-year follow-up
from the "Coronary Artery Risk Development in Young Adults Study" indicate no
association between baseline alcohol consumption and incident hypertension
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(Halanych et al., 2010). A systematic review and meta-analysis indicated an increased
risk of hypertension in men with heavy alcohol consumption and a trend toward
increased risk of hypertension with low and moderate alcohol consumption in the
general population (Briasoulis et al., 2012).
Most studies involving PLHIV indicate either no significant difference in prevalence
rate of alcohol consumption between hypertensives and non-hypertensives (Dimala et
al., 2016) or no association between alcohol consumption and hypertension in PLHIV
(Arruda Junior et al., 2010; Kagaruki et al., 2014; Mashinya et al., 2015; Mutede et al.,
2015; Kwarisiima et al., 2016). A study by Ikeda et al. (2013), however reported of an
increased odds of hypertension in PLHIV who consume alcohol compared with non-
consumers of alcohol.
2.3.5.1.6 Smoking of tobacco
Tobacco smoking is a known major risk factor for the development of CVDs in the
general population (Linneberg et al., 2015). However, whilst some observational
studies have reported lower systolic and diastolic blood pressure levels and a lower risk
of hypertension in current smokers compared with non-smokers (Brummett et al., 2011;
Cheng et aI., 2012; Linneberg et al., 2015), others have reported a decrease in blood
pressure levels following smoking cessation (Ward et al., 1993; Hatsukami et al., 2005).
Studies on the effect of smoking on the risk of hypertension in PLHIV have reported
conflicting results. A study by Denue et al. (2012) in PLHIV, reported of significant
difference between hypertensives who smoke (11.1 %) and non-hypertensives who
smoke (2.5%). The MACs study also reported of smoking being associated with
systolic hypertension but not diastolic hypertension (Seaberg et al., 2005). Results from
a study by Mutede et al. (2015), indicate of an increased odds of hypertension in PLHIV
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who smoke compared with non-smokers (adjusted OR=S.06 [9S% CI, 2.20-11.66]). A
review of most studies however indicated of no association between smoking and
hypertension in PLHIV (Bergersen et aI., 2003; Jerico et al., 2005; Crane et al., 2006;
Palacios et al., 2006; Arruda Junior et aI., 2010; Ikeda et al., 2013; Manner et al., 2013b;
Kagaruki et al., 2014; Antonello et al., 2015; Dimala et al., 2016).
2.3.5.1. 7 Regular physical activity
Several epidemiological studies have demonstrated the association of regular physical
activity with the reduction in risk of hypertension in the general population (Diaz and
Shimbo, 2013). Diaz and Shimbo (2013), in a review proposed several mechanisms by
which regular physical activity may reduce the incidence of hypertension. These
include decrease in oxidative stress, decrease in the RAS, decrease in inflammation and
decrease in body weight/body mass accompanied by increase in endothelial function,
increase in renal function and increase in sodium handling and angiogenesis (Diaz and
Shimbo, 2013). Regular physical activity is therefore one of the recommended cardinal
points in the prevention and management of hypertension (Chobanian et al., 2003).
Studies on the association between regular physical activity and hypertension in PLHIV
have reported of mixed results. Dimala et al. reported of lack of significant difference
between hypertensive and non-hypertensives in terms of proportions of PLHIV who
lack regular physical activity in both ART-naive and ART-experienced individuals
(Dimala et al., 2016). Other studies have similarly reported of lack of association
between regular physical activity and hypertension in PLHIV (Ikeda et al., 2013;
Kagaruki et al., 2014; Mashinya et aI., 201S). However, Mutede et aI. (2015), reported
of an increased odds of hypertension in PLHIV who are physically inactive compared
with those physically active (pOR=3.16 [9S% CI, 1.69-S.85]).
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2.3.5.1.8 Fruit and vegetable consumption
One of the recommendations of the WHO to prevent the onset of hypertension is regular
fruit and vegetable consumption. However, most of studies that determined the factors
associated with hypertension in PLHIV did not report of fruit/vegetable consumption.
Arruda Junior et al. (2010), however reported a decreased odds of regular vegetable
consumption associated with hypertension (OR= 0.69 [95% CI, 0.49-0.98], p=0.039),
but no association between regular fruit intake and hypertension.
2.3.5.1.9 Family history of cardiovascular disease
The risk ofCVD has been known to run in families and longitudinal studies have shown
that individuals with a parental history of hypertension are at an increased risk of
developing hypertension in the general population. These individuals are classified as
high-risk for target intervention in the prevention of hypertension (Whelton et al., 2002;
Wang et al., 2008).
Studies on the association between family history of CVD and hypertension in PLHIV
indicate that individuals with family history of CVD are at an increased odds of
hypertension compared with individuals with no such family history (OR=I.68 [95%
CI, 1.15-2.47], p=0.008) (Arruda Junior et al., 2010); (adjusted OR=1.58 (95% CI,
1.18-2.12) (De Socio et al., 2014). However, studies by Thiebaut et al. (2005) and
Dimala et al. (2016) indicate no such association between family history of CVD and
hypertension in PLHIV.
2.3.5.2 Anthropometric and biochemical/metabolic factors
Studies have determined the association between hypertension and anthropometric
indices like body mass index, waist circumference and waist-to-hip ratio in PLHIV.
Some biochemical/metabolic factors like fasting plasma glucose, lipid profile and
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creatinine clearance rate have also been investigated to determine their association with
hypertension in PLHIV.
2.3.5.2.1 Body mass index (general obesity)
Body mass index (BMI) is obtained by dividing body weight (in kilogram) by height
(in metres) squared i.e. BMI= body weight (in kg)/ (height in m):'. BMI is used as an
index/marker of general obesity in terms of being under weight (BMI < 18.S kg/m2),
normal weight (BMI between 18.S kg/rrr' and 24.9 kg/rrr'), overweight (BMI between
2S.0 kg/m2 and 29.9 kg/nr') and general obese (BMI 2: 30.0 kg/rrr') (WHO, 1995).
Observational studies have established general obesity as a known risk factor for
hypertension in the general population (Brown et aI., 2000; Sakurai et aI., 2006) and
clinical trials have shown lowering in blood pressure after reduction in weight (Stevens
et al., 2001).
Similarly, most studies conducted in PLHIV have indicated an association between
BMI and hypertension. Studies from both MACS (Seaberg et aI., 200S) and D:A:D
(Friis-MoUer et aI., 2003; Thiebaut et aI., 200S) have strongly associated BMI 2:25.00
kg/m2 with increased odds of hypertension. Arruda Junior et aI. (2010), and De Socio
et al. (2014), also reported increased odds of hypertension for BMI 2:25.00 kg/m2
(adjusted OR=5.51 [95% CI, 3.36-9.17], p<O.OOl; adjusted OR=2.S2 [95% CI, 1.91-
3.33] respectively). Other studies also indicated an increased odds of hypertension per
each unit increase in BMI (adjusted OR=1.1, [9S% CI, 1.1-1.2], P<O.OOI) (Medina-
Tome et aI., 2012). Similar trend of association has been reported by other studies
(Jerico et aI., 2005; Crane et aI., 2006).
Studies conducted in SSA have also reported an association between BMI and
hYpertension in PLHIV. Bloomfield et al. (2011), reported of PLHIV with BMI 2:25.00
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kg/rrr' having an increased odds of hype liens ion in both men (OR=2.42 [95% CI, 1.88-
3.09]) and women (OR=1.80 [95% CI, 1.50-2.16]). Several other studies have also
reported increased odds of hypertension in overweight/obese HIV -positive patients
compared with underweight/normal HIV -positive patients (Mutede et aI., 2015; Dimala
et aI., 2016; Kwarisiima et aI., 2016; Njelekela et aI., 2016). Other studies have also
reported the association of overweight/obesity with hypertension in PI-experienced
patients (Bloomfield et aI., 2011) and in both ART-naive and ART-exposed patients
(Kagaruki et aI., 2014).
2.3.5.2.2 Waist-to-Hip ratio and waist circumference (abdominal obesity)
Waist-to-hip ratio (WHR) is used as an anthropometric marker of abdominal obesity.
Abdominal obesity, also known as central obesity refers to the accumulation of excess
abdominal fat around the stomach and abdomen, which is likely to have a detrimental
effect on health status. Abdominal obesity is associated with increased risk of
hypertension, dyslipidaemia, heart disease, insulin resistance, type 2 diabetes and an
overall increase in risk of developing CVDs and death in the general population (Anjana
et aI., 2004; Cameron and Zimmet, 2008; Westphal, 2008). WHR is calculated as the
ratio of the waist circumference to the hip circumference with ratios> 0.9 (men) and>
0.85 (women) considered as evidence of abdominal obesity (WHO, 2000; NCEP, 2002;
Yusufet aI., 2004).
Studies on WHR as a risk factor of hypertension in PLHIV have been few but with
conflicting results. Whilst Bergersen et al. (2003), and De socio et al. (2014) reported
of increased odds of hypertension in abdominal obese PLHIV (OR=2.22 [95% CI, 1.18-
4.17], p=O.OOI and adjusted OR=1.42 [5% CI, 1.03-1.97] respectively), other studies
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reported of lack of association between WHR and hypertension in PLHIV (Hejazi et
al., 2013 ; Mashinya et al., 2015; Dimala et al., 2016).
Waist circumference (WC) is also used in a measure of abdominal obesity. Absolute
waist circumference of > 102 em (in men) and >88 cm (in women) is classified as
abdominal obesity (WHO, 2000; Yusuf et al., 2004). Arruda Junior et al. (2010),
reported of abdominal obesity associated with increased odds of hypertension in
PLHIV (OR=3.96 [95% CI, 2.57-6.18], p< 0.0001). Using a cut-off point of male 294
em and female >80 em as measure of abdominal obesity, Kagaruki et al. (2014), also
reported of abdominal obesity association with increased odds of hypertension in both
ART-naive (OR=1.64 [95% CI, 1.03-2.62], p=0.023) and ART-exposed patients
(OR=2.37 [95% CI, 1.13-5.00], p=0.023). However, a study by Hejazi et al. (2013),
reported of no association between abdominal obesity and hypertension in PLHIV.
2.3.5.2.3 Diabetes
Diabetes and hypertension are both indices used in the classification of the metabolic
syndrome. Metabolic syndrome is a cluster of interrelated cardio-metabolic
abnormalities comprising hyperglycaemia, elevated blood pressure, dyslipidaemia
(elevated triglycerides and reduced high-density lipoprotein cholesterol) and abdominal
obesity with abdominal obesity being a key component of the syndrome (Nguyen et al.,
2016). Metabolic syndrome is used as a predictor of CVDs, type 2 diabetes and
hypertension (Cheung et al., 2007; Cheung et al., 2008b) and its presence is increasing
been seen in PLHIV on ART.
Diabetes is directly linked to atherosclerosis, which can lead to hypertension and other
CVDs. Studies have shown that in the general population, the incidence of diabetes in
hypertensive patients is disproportionately higher and vice-versa (Gress et al., 2000;
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Cheung et al., 2008a; Ferrannini and Cushman, 2012). The rate at which these two
conditions occur together has led to several suggestions that they both share common
aetiology being obesity, physical inactivity and the metabolic syndrome (Cheung, 2010;
Cheung and Li, 2012).
In PLHIV, reports of the association between diabetes and hypertension have been
mixed. This could be attributed to different studies reporting of different fasting plasma
glucose (FPG) level used as criterion in the assessment of diabetes. Bergersen et al.
(2003), used a classification of diabetes to be FPG of>5.1 mmoliL and/or the taking of
anti-diabetic medication and reported of no association between diabetes and
hypertension in PLHIV. Hejasi et al. (2013) used a classification of diabetes as FPG
2:6.1 mmol/L and/or the taking of anti-diabetic medication and reported of no
association between diabetes and hypertension in HIV -positive patients. Other studies
have also reported of lack of association between diabetes and hypertension in PLHIV
(Baekken et al., 2008; Antonello et al., 2015).
On the other hand, several other reports have associated diabetes with hypertension in
PLHIV. Arruda Junior et al. (2010), used a criterion of diabetes as FPG > 100mg/dL
reported of diabetes been associated with increased odds of hypertension (OR=3.62
[95% CI, 1.93-7.00], p< 0.001). De Socio et al. (2014) used a criterion of FPG > 126
mg/dL also reported of increased odds of hypertension in HIV -positive individuals who
are diabetic (adjusted OR=2.66 [95% CI, 1.65-4.27]). Medina-Tome et al. (2012), and
Krauskopf et al. (2013), both reported of increased odds of hypertension in HIV-
positive individuals who are diabetic (adjusted OR=1.82 [95% CI, 1.28-2.57]; adjusted
OR=1.82 [95% CI, 1.28-2.57] respectively).
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2.3.5.2.4 Dyslipidaemia
Dyslipidaemia is an important risk factor for CVD (Halperin et al., 2006; Di
Angelantonio et al., 2009) and it refers to a disorder of the lipoprotein metabolism,
classified as either over-production of lipoprotein (hyperlipidaemia) or under-
production of lipoproteins (hyperlipidaemia). Most dyslipidaemia is characterised by
hyperlipidaemia marked by elevated serum concentrations of total cholesterol, low-
density lipoprotein cholesterol (LDL-C) and triglycerides with a corresponding
decrease in high-density lipoprotein cholesterol (HDL-C). Reduced concentration level
of HDL-C is also regarded as an independent significant risk factor for CVD (Jerico et
aI.,2006).
Several studies have associated dyslipidaemia with hypertension in the general
population. Elevated serum concentration ofHDL-C shows an independent and inverse
association with the incident of hypertension (Laaksonen et al., 2008). Elevated serum
concentrations of total cholesterol, LDL-C and triglycerides have also been shown to
increase the risk of hypertension in the general population (de Simone et aI., 2006;
Halperin et al., 2006; Laaksonen et al., 2008). Although the pathophysiology of
dyslipidaemia in hypertension is not fully understood, key mechanisms postulated
include atherosclerosis (Oparil et al., 2003), renal microvascular injuries (Schaeffner et
al., 2003) and alterations in nitric oxide and endothelial functions (Nofer et al., 2002).
Review of several studies determining the association between components of the lipid
profile and hypertension in PLHIV show contrasting results. Antonello et al. (2015)
reported of no association between dyslipidaemia and hypertension in PLHIV. Whilst
some studies have associated elevated levels of total cholesterol with hypertension
(Bergersen et al., 2003; Thiebaut et al., 2005; Baekken et al., 2008; Diouf et al., 2012;
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Kagaruki et al., 2014), others have reported of no such association (Palacios et al., 2006;
Arruda Junior et al., 2010; Medina-Tome et al., 2012; Manner et al., 2013b), Similarly,
whilst some studies associate elevated triglycerides with increased odds of hypertension
(Bergersen et al., 2003; Palacios et al., 2006; Arruda Junior et al., 2010), others have
reported no association between elevated triglycerides and hypertension (Thiebaut et
al., 2005; Dioufet al., 2012; Medina-Tome et al., 2012; Hejazi et al., 2013) in PLHIV.
Kagaruki et al. (2014), however reported that elevated triglycerides level is associated
with hypertension in ART-naive PLHIV (OR=1.96 [95% Cl, 1.22-3.16]), but not in
ART-experienced PLHIV (OR=0.94 [95% CI, 0.49-1.83]). Kagaruki et al. (2014), also
reported of no association in both ART-naive and ART-experienced PLHrv between
levels of HDL-C and hypertension and this is similar to other reports (Bergersen et al.,
2003; Palacios et al., 2006; Arruda Junior et al., 2010). Other studies have also reported
of reported of no association between elevated levels of LDL-C and hypertension
(Palacios et al., 2006; Arruda Junior et al., 2010). Despite the fact that different studies
used different cut off-points in the classification of elevated lipid levels, it seems that
there is no conformity in the association between serum lipid levels and hypertension
in PLHIV.
2.3.5.2.5 Renal impairment
Hypertension and chronic kidney disease (CKD) are so related that hypertension is seen
as both a cause and consequence ofCKD (Singh, 2013). Renal dysfunction is associated
with CVDs (Sarnak et al., 2003) and CKD is an important independent risk factor for
cardiovascular diseases (Lasserson et al., 2016). The prevalence of hypertension in
CKD patients is 80-85% (Whaley-Connell et al., 2008) and this prevalence increases as
the CKD progresses (USRDS, 2010). The mechanisms through which renal dysfunction
mediates in the pathogenesis of hypertension include sodium and volume homeostasis,
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RAS, oxidative stress and nitric oxide antagonism, endothelial dysfunction and
sympathetic nervous system and renal dopaminergic system (Tedla et al., 2011;
Monhart, 2013; Singh, 2013).
Although there is strong relationship between renal impairment and hypertension, only
few studies have determined this association in PLHIV. In studies reviewed, estimated
glomerular filtration rate (eGFR) or change in creatinine clearance was not associated
with hypertension in PLHIV (Baekken et al., 2008; Denue et al., 2012; Manner et al.,
2013b). Peck et al. (2014), in an observational study involving both HIV-positive and
HIV -negative participants indicated a prevalence of renal impairment (defined as eGFR
<60 mLimin) to be 10.7% in ART-exposed, 12.6% in ART-naive and 13.1% in HIV-
negative individuals. The study also reported of an association between renal
impairment and higher grades of hypertension in ART -exposed individuals but not in
ART-naive individuals (Peck et al., 2014).
2.3.5.3 HIV/ART-related factors
With the onset of the ART era, studies that have determine factors associated with
hypertension in PLHIV have included HIV IART -related like HIV -positive status, viral
load, CD4+ T cell count and the type of ARVs administered.
2.3.5.3.1 HIV, viral load and duration of infection
Although some studies have implicated HIV status been associated with hypertension,
there is still the need to know the direction of this association. The mechanisms
postulated to mediate the higher prevalence of hypertension in PLHIV include
atherosclerosis, coagulation disorder, chronic inflammation and lipid disturbance
(Baker and Lundgren, 2011; Boccara et al., 2013). A few studies have associated HIV-
positive status with higher odds of hypertension compared with HIV -negative status
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(Gazzaruso et al., 2003; van Zoest et al., 2016), whilst others have reported no such
association (Bergersen et al., 2003; Jerico et al., 2005; Baekken et al., 2008).
In SSA, a review of such studies has also shown mixed results. Whilst some studies
report of no association between HIV itself and hypertension (Schwartz et al., 2012;
Mashinya et al., 2014; Ogunmola et al., 2014), others report of an association (Peck et
al., 2014). Malaza et al. (2012), reported of no association between HIV infection and
hypertension in men but lower odds of hypertension in women living with HIV
compared with their counterparts with HIV -negative status. Interestingly most of the
studies conducted in SSA reported oflower odds of hypertension in HIV infected adults
compared with their uninfected control adults (Scholten et al., 2011; Schutte et al.,
2012; Kayima et al., 2015). A large population study involving 65,000 adults also
reported of lower odds of hypertension in HIV -positive adults compared with HIV-
negative adults (OR=0.82 [95% CI, 0.70-0.90], p=0.002) (Kwarisiima et al., 2016). A
systematic review with meta-analysis of publication ofCVDs in SSA reported oflower
odds of hypertension in HIV infected adults compared with HIV -negative adults
(Dillon et al., 2013). These results clearly indicate that whilst the risk of hypertension
is lower in HIV -infected individuals in SSA compared with HIV -negative individuals
it is contrary to what has been reported in developed countries.
In terms of the HIV -infection duration on the risk of hypertension, Medina-Tome et al.
(2012), and Manner et al. (2010) both reported of an increased odds of hypertension in
patients as the duration of the HIV -infection increases. Other researchers have also
reported of increasing risk of hypertension in PLHIV as the duration of the HIV-
infection increases (Krauskopf et al., 2013; Manner et al., 2013b; De Socio et al., 2014).
On the other hand, several other studies have reported of no association between HIV-
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positive duration and the risk of hypertension in PLHIV (Bergersen et al., 2003; Jerico
et al., 2005; Palacios et al., 2006; Baekken et al., 2008; Arruda Junior et al., 2010;
Denue et al., 2012; Ikeda et al., 2013; Hejazi et al., 2013 ; Dimala et al., 2016).
Viral load is used as marker of immune suppression in PLHIV. Few studies which have
determined the relationship between viral load and hypertension have reported of no
association between viral load and hypertension in PLHIV (Bergersen et al., 2003;
Thiebaut et al., 2005; Crane et al., 2006; Arruda Junior et al., 2010; Medina-Tome et
al., 2012; Krauskopf et al., 2013; Antonello et al., 2015; Kwarisiima et al., 2016).
2.3.5.3.2 CD4+ T-cell count
Several studies have been conducted to determine the association between CD4+ T-cell
count and the incidence of hypertension and CVDs. Chronic inflammation in PLHIV
has been shown to destabilize atherosclerotic plaques, which leads to CVDs (Kaplan et
al., 2008; Hsue et al., 2012). Low CD4+ T-cell count has also been associated with
higher risk of CVDs (Kaplan et aI., 2008; Lichtenstein et aI., 2010; Triant et al., 2010;
Manner et al., 2013b).
Studies have shown that microbial translocation from the gut into the systemic
circulation during HIV infection leads to an array of immune activation resulting in
decline in CD4+ T-cells and inflammation. Microbial translocation has been shown to
be associated with hypertension in PLHIV (Manner et al., 20 13a). Lipopolysaccharides
have also been associated with endothelial dysfunction and atherosclerosis, both of
which are involved in the pathogenesis of hypertension (Dauphinee and Karsan, 2006;
Solages et al., 2006; Oliviero et al., 2009). Studies have also associated low CD4+ T-
cell count with arterial stiffness (Ho et al., 2010) and atherosclerosis (Hsue et aI., 2004),
both which involved in the pathogenesis of hypertension. Despite these mechanisms
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postulated to be involved in the association of low CD4+ T-cell count with
hypertension, several observational studies indicate of no association between low
CD4+ T-cell count and hypertension (Bergersen et al., 2003; Jerico et al., 2005;
Thiebaut et aI., 2005; Palacios et aI., 2006; Arruda Junior et aI., 2010; Medina-Tome et
aI., 2012; Ikeda et aI., 2013; Hejazi et al., 2013 ; Antonello et aI., 2015).
Studies conducted in SSA have also not associated low CD4+ T-cell count with
hypertension (Denue et al., 2012; Diouf et al., 2012; Ogunmola et aI., 2014; Okello et
aI., 2015; Sander et al., 2015; Dimala et aI., 2016). However, a few studies have
associated low CD4+ T-cell count «200 cells/ul.) with increased odds of hypertension
(adjusted OR=1.60 [95% CI, 1.05-2.41]) (De Socio et aI., 2014). Similarly, other
studies have associated Nadir CD4+ T-cell count of < 50 cells/uL with increased odds
of hypertension (adjusted OR=2.31 [95% CI, 1.17-4.56], p=0.015) (Manner et aI.,
2013b). Two studies actually associated higher levels of CD4+ T-cell count with
increasing odds of hypertension (Peck et al., 2014; Njelekela et aI., 2016).
2.3.5.3.3 Antiretroviral therapy
Worldwide, several studies have been conducted in PLHIV in the area of hypertension
in PLHIV. However, the association between ART and hypertension in PLHIV remains
debatable with conflicting results. In a Norway study, antiretroviral treatment duration
appeared to be an associated factor for hypertension, but the roles of the various ARVs
were not investigated (Baekken et al., 2008). A study conducted in Spain indicated that
a rise in blood pressure was reported after 48 weeks of ART (Palacios et aI., 2006) and
another study reported by Jerico et aI. (2005) indicated no association between ART
and hypertension. However, Gazzaruso et aI. (2003) reported of a higher prevalence of
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hypertension in patients on ART compared with ART-naive patients (34.2% versus
11.9%; p<O.OOOI).
A systematic review and meta-analysis of 39 studies (which included 11 studies from
SSA) involving 44,903 PLHIV indicated that the prevalence of hypertension in ART-
exposed is 14.5% whilst that of ART-naive is 10.5% regardless of the socio-
demographic differences between the studies (Nduka et al., 2016a). The review also
indicated increased odds of hypertension in ART-exposed patients (OR=1.68 [95% CI,
1.35-2.10], P=81.5%) compared with ART-naive patients.
In the SSA region, divergent results have also been reported in terms of the relationship
between ART and hypertension. Table 5 shows a summary of studies conducted in SSA
on the relationship between ART and hypertension. Whilst studies from Ghana (Ngala
and Fianko, 2014), Cameroon (Nsagha et al., 2015; Dimala et al., 2016) and Nigeria
(Nduka et al., 2016b) and indicate an association between ART and hypertension in
PLHIV, a study from Nigeria reported otherwise (Ogunmola et al., 2014). In terms of
prevalence, whilst studies from Botswana (Shapiro et al., 2012), Nigeria (Muhammad
et al., 2013), Cameroon (Ekali et al., 2013; Dimala et al., 2016) indicate of significant
difference in hypertension prevalence between ART-exposed and ART-naive study
participants, other studies (Edward et a!., 2013; Dimodi et al., 2014; Sander et aI., 2015)
reported otherwise of difference in hypertension prevalence between ART -exposed and
ART-naive study participants. A study from Tanzania reported of differences in
prevalence of hypertension between ART-exposed and ART-naive patients (30.0% vs.
21.9% respectively, p=O.OI0) (Kagaruki et aI., 2014)
Worth noting is that a retrospective cohort study in Kenya (Bloomfield et aI., 2011)
which involved a large sample size of 12,194 PLHIV did not report of an association
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between length of PI usage and hypertension. Table 4 shows a summary of the studies
in SSA reviewed on the epidemiological relationship between ART and hypertension.
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Table 5. Studies assessing the relationship between highly active anti-retroviral therapy and hypertension in PLHIV in sub-Saharan Africa.
Sample size % Relationship between ART and hypertension/blood
Country Authors Year Study-design ofPLHIV Female pressure values
Botswana Hypertension prevalence significantly higher In ART-Shapiro et al. 2012 Cross-sectional 62 100 exposed compared with ART-naive
Hypertension prevalence significantly higher In ART-
Ekali et al. 2013 Cross-sectional 143 72.0 exposed compared with ART-naive
No significant difference In hypertension prevalence
Dimodi et al. 2014 Cross-sectional 463 74.7 between ART-exposed and ART-naive
Cameroon _N~sa~g~h~a_e~t~a_l.2_0~1~5C_r~0_ss_-~se_c_tl~2·_01_5n~a_I 7_4~.9 A__R_T_i_s_a_s~so__cw__iai_t_t_he_dh~y~p_e_rt_e_ns~i_on~ __
Hypertension prevalence significantly higher in ART-
Dimala et al. 2016 Cross-sectional 200 70.0 exposed compared with ART-naive; ART is associated
with hypertension
Ghana Ngala and Fianko 2014 Cross-sectional 305 68.2 ART is associated with hypertension
Kenya Bloomfield et al. 2011 Cohort 12,194 64.8 No association between length of PI use and hypertension
No significant difference In hypertension prevalence
Edward et al. 2013 Cross-sectional 265 67.5 between ART-exposed and ART-naive
Hypertension prevalence significantly higher in ART-
Nigeria Muhammad 2013 Cross-sectional 200 53.0 exposed compared with ART-naive
No significant difference In hypertension prevalence
Ogunmola et al. 2014 Cross-sectional 250 62.4 between ART-exposed and ART-naive
ART is related to increase in both systolic and diastolic
Nduka et al. 2016a Cross-sectional 406 68.5 blood pressure values using PSM analysis
Tanzania Hypertension prevalence significantly higher in ART-Kagaruki et al. 2014 Cross-sectional 671 70.5 exposed compared with ART-naive
No significant difference In hypertension prevalence
Uganda Sander et al. 2015 Cross-sectional 426 71.0 between ART-exposed and ART-naive
ART=Antiretroviral Therapy; PI=protease inhibitor; PLHIV=Persons Living with HIV; PSM=Propensity score-matching
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2.3.6 Pathogenesis/pathophysiology of hypertension in PLHIV
In addition to the known risk factors of hypertension in the general population, HIV
itself and ARVs used in ART have been implicated in the pathogenesis and
pathophysiology of hypertension and other CVDs in PLHIV. This interaction is further
made complex with environmental and genetic factors. Figure 4 shows a hypothetical
model postulated by Hemkens and Bucher (Hemkens and Bucher, 2014) showing the
various patho-physiological pathways by which HIV and ART promote hypertension
and other cardiovascular diseases in PLHIV.
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Pathophysiology of eVD In HIV-Infected persons
Dyslipidemia, Lipodystrophy, Insulin Resistance
Adipose Tissue and
Liver Dysfunction
Endothelial calls and vascular
smooth muscle cells
?lROS ?lRAS ::.INO
7IVCAM-1
Hypertension, Atherosclerosis. Myocardial Infarction
Figure 4. Hypothetical model for the pathogenesis of cardiovascular disease in HIV-
infected persons taking antiretroviral therapy Source: (Hemkens and Bucher, 2014)
"HIV and antiretroviral treatment (ART) might affect cardiovascular disease (CVD) through
various pathophysiological pathways (together with environmental and genetic factors). There
is a very complex interaction of various related factors. HIV and ART have direct effects on
adipose tissue and liver function with subsequent dyslipidemia, lipodystrophy and insulin
resistance. Other direct impact includes effects on endothelial cells and vascular smooth muscle
cells leading to vascular and endothelial dysfunction with subsequent hypertension,
atherosclerosis and myocardial infarction. Continuous immune activation and viral replication
might lead to a permanent T cell activation, which might also be affected by a reactivation of
other viruses, e.g. Cytomegalovirus (CMV). ART and HIV might also stimulate a chronic status
of inflammation and have a complex interaction with coagulation factors. These pathways itself
are more or less interrelated and mediate indirect effects ofHIV and ART on CVD" (Hemkens
and Bucher, 2014)
Abbreviations: ART=Antiretroviral therapy; hs-CRP: high-sensitivity; C-reactive protein; F
VII: Factor VII; FFA: Free fatty acids; HDLc: High-density lipoprotein cholesterol; IL6:
Interleukin 6; LPS: lipopolysaccharide; NO: Nitrogen oxide; PAl-I: Plasminogen activator
inhibitor type 1; PPARy: peroxisome proliferator-activated receptors; RAS: Renin angiotensin
system; ROS: Reactive oxygen species; sCD14: Soluble CD14; sCD163: Soluble CD163.
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2.3.6.1 The HIV-infection
The HIV infection itself can promote atherosclerosis through immune activation,
chronic inflammation, coagulation disorders and lipid disturbances as follows (Baker
and Lundgren, 2011; Boccara et al., 2013);
• The virus has been reported to enhance endothelial injury resulting in
endothelial dysfunction through the production of molecules which
enhance angiogenesis like adhesion molecules (de Gaetano Donati et al.,
2004) and HIV Tat protein (Gresele et al., 2012).
• HIV also stimulates the production of human vascular smooth muscle cells,
which is implicated in the development of atherosclerosis (Eugenin et al.,
2008).
• The onset of coagulation disorders (a "prothrombic state") which is
associated with HIV leads to increased levels of D dimers, fibrinogen,
factor VII, von Willebrand and tissue factors and abnormal platelet
reactivity (Baker et al., 2011; Gresele et al., 2012).
• HIV infection is also associated with known atherogenic dyslipidemia
observed as increased levels of triglycerides (results of impaired lipase
activity) and decreased levels ofHDL-C and correlated high concentration
of cytokines (Grunfeld et al., 1992).
2.3.6.2 Anti-retrovirals used in ART
Although it remains debatable due to conflicting results, several reports have reported
the association between ARVs used in ART and hypertension/CVDs in PLHIV. The
pathogeneses involved in ARVs inducing hypertension (and other CVDs) in PLHIV
are as follows;
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• The use of ART leads to immune activation and may lead to low-grade
inflammation, which can promote atherosclerosis (Fisher et al., 2006; Coll
et al., 2007; Boccara et al., 2013).
• During immune activation there is an increased translocation of intestinal
bacterial from the gut into the systemic circulation which impacts on the
promotion of atherosclerosis (Redd et al., 2011).
• Several studies have implicated ART involvement in lipid and glucose
metabolism leading to adipocyte dysfunction which results III
lipodystrophy syndrome (Grinspoon and Carr, 2005; Capeau, 2007;
Boccara et al., 2010). Protease inhibitors (PIs) have also been associated
with activation of the adipocyte RAS (Boccara et al., 2010)
• Studies have also reported of PIs inducing endothelial injury, and this has
been associated with dyslipidaemia, oxidative stress and senescence
(Lefevre et al., 2010). However, contradicting results from the AIDS
Clinical Trials Group 5152 indicate that, endothelial dysfunction rather
improved over a ART course of 24 weeks (Torriani et al., 2008). This has
led to the postulation of the different effect of PIs on the risk of CVDs
dependent on the length of usage, be it short term or long term.
2.4 Propensity score-matching analysis
Propensity score-matching (PSM) analysis refers to a statistical method of estimating
average treatment effect on an outcome in a bid to infer a causal link. The conceptual
foundation of PSM analysis was first propagated by Donald B. Rubin in what is now
known as the "Rubin causal model" (RCM) after a series of publications on methods to
remove bias and estimate causal effect in observational studies (Rubin, 1974;
Rosenbaum and Rubin, 1983a; Rosenbaum and Rubin, 1983b; Rosenbaum and Rubin,
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1984; Holland, 1986; Rubin, 2007; Austin, 2011b). The Rubin causal model is based
on the Neyman potential outcomes framework and the idea that depending on a
particular treatment assignment, very individual or participant has a potential outcome,
which is expressed in the form of counterfactual conditional statements. The model
holds that the causal effect of being exposed to a treatment as opposed to being
unexposed to that treatment at any given time, is the difference between the outcome
measure with and without treatment assuming no significant difference in the baseline
characteristics between the two groups (Rubin, 1974; Imbens, 2004).
PSM analysis involves the use of an estimated propensity score which is a single score
assigned to each individual in the treatment group and it represents the conditional
probability of receiving a treatment, given a set of observed covariates or vector of
covariates (Garrido et al., 2014). The ultimate aim of creating a propensity score is to
be able to balance the observed covariates between individuals in the treatment group
and individuals in the comparison group. Thus, PSM analysis mimics randomised
control trial using statistical balancing of baseline covariates between the treated group
and the untreated/comparison group and this makes it easier to isolate and thereafter
infer high level of effect of a treatment or intervention on an outcome of interest.
Although PSM analysis has become very popular in the area of developmental
economics and econometrics, its usage in the health sciences is gradually picking up
due to its ability to answer most health interventions impact assessment/analysis. The
interest in the use of PSM analysis in health research is growing as a valuable tool to
infer or otherwise a plausible causal effect of treatment i.e. cause-effect relationships,
especially in situations where ReT is not practicable due to logistical constraints or
ethical reasons (Rosenbaum and Rubin, 1985; Austin, 2011a; Austin, 2011b). The
concept of causation in epidemiology has gone through a period of evolution and
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expansion and has gone beyond and above the simple relationships established by
"Hill's Criteria of Causation" (Hill, 1965; Lucas and McMichael, 2005; Mirtz et al.,
2009) to the use of statistical methods in testing causal hypothesis (Rosenbaum and
Rubin, 1985; Austin, 2011 b; Austin, 2011 a). PSM analysis is thus a useful tool in
observational studies where there is a need to infer a causal relationship between
treatment and an outcome.
The method of matching individuals in a treatment group to individuals in a comparison
group based on an estimated propensity score can account for selection bias due to
observable characteristics used in the matching process. Rosenbaum and Rubin
(Rosenbaum and Rubin, 1983b) demonstrated that this matching gives an unbiased
estimate of the effect treatment. Despite these demonstrations, three caveats and
assumptions are observed when using PSM analysis. The first is the independent and
identically distributed sampling assumption (i.i.d) which ensures that for a given
dataset, the potential outcome and treatment status of each individual in the dataset is
not dependent on the potential outcome and treatment status of other individuals in the
dataset. The second assumption is the conditional independence assumption (c.i.),
which means that once the propensity has been estimated and used to balance all
observable covariates, the potential outcomes are independent of treatment
assignment/model. The third assumption is the overlap assumption, which ensures that
each individual has a positive probability of receiving treatment. However, the non-
inclusion of unobservable covariates or "hidden bias" which may affect treatment
allocation is a drawback on the PSA method. The extent of this "hidden bias" can be
estimated by doing post-estimation analysis using the rbound syntax in Stata.
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2.4.1 Steps used in propensity score-matching analysis
There are five steps used in propensity score-matching analysis after which the effect
of treatment on an outcome is estimated (Austin, 2011 b; Garrido et al., 2014). The five
steps are
1. selection of covariates to include in estimating propensity scores for each
study participant
2. balance of estimated propensity scores across treatment and comparison
groups
3. balance of covariates across treatment and comparison group within blocks
of estimated propensity scores
4. choosing a matching or weighting strategy to match each treated study
participant to a control based on individually estimated propensity score
5. assessment of balance of covariates after matching/weighting the samples
using the estimated propensity scores
2.4.1.1 Selection of covariates to include in estimating propensity score
The first step in PSM analysis is to select the covariates to be used in estimating
propensity score for each study participant. This selection process is very important and
involves both theorised and hypothesised approaches. Variables associated with both
treatment and outcome are considered as confounders and included in the selection.
Variables associated with the outcome but not the treatment are also included to reduce
bias (Austin, 2011 b). Although it is encouraged to include all variables that are
potentially related to the outcome, in smaller dataset the inclusion of potentially
irrelevant covariates may introduce too much "noise" and obscure any reduction in bias
that would have been achieved by their inclusion (Austin, 2011 b). However, the
inclusion of variables that are hypothesised to be associated with the treatment but not
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with the outcome can decrease estimation of the treatment effect (Austin, 2011 b;
Garrido et al., 2014). Of caution is to exclude covariates that are affected by the
treatment and also covariates that do predict treatment status perfectly as the
distribution of covariates needs to be balanced between the treatment group and the
comparison group. After the selection of covariates, a logit or pro bit regression with
treatment as the outcome variable and the potential confounders (covariates) as the
explanatory variables is used to estimate the propensity score for each individual in the
study population.
2.4.1.2 Balance of estimated propensity score across treatment and
comparison groups
After estimating the propensity score for each individual, the estimated propensity
scores are assessed to have similar distribution across treatment and comparison groups.
This is to ensure that there is overlap in the range of the estimated propensity scores
across treatment and comparison groups, commonly called "common support''.
"Common support" is assessed SUbjectively by the examination of a graph showing the
range of the estimated propensity score across the treatment and comparison using a
box plot. In addition, the estimated propensity scores are tested to have similar
distribution in the treated and comparison group using a t-test. This is achieved by
splitting the study participants into quintiles and the balance of the estimated propensity
scores tested for each quintile by at-test.
2.4.1.3 Balance of covariates across treatment and comparison group within
blocks of the estimated propensity score
This step is done to assess that each covariate is balanced between treatment and
comparison group. Some covariates at this stage may be dropped, re-categorised,
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interaction terms introduced or higher-level splines introduced. Although there is no
agreeable test for balance of covariates, Austin (Austin, 2009) and Stuart et al. (Stuart
et al., 2013) have proposed that the maximum standardised difference for each covariate
should range between 10% and 25%.
2.4.1.4 Choosing a matching or weighting strategy
The choice of a matching or weighting strategy is a trade-off between quality and
quantity. Notable strategies include regression adjustment, inverse-probability
weighting, augmented inverse-probability weighting and inverse-probability-weighted
regression adjustment. Others are nearest-neighbour matching, radius matching,
propensity-score matching and kernel-weighting/matching strategies. There is no
universal "best" strategy as each strategy has its own merits and limitations. These
merits and limitations are in terms of how close a match is acceptable, whether every
individual in the treatment group should have one or matches in the comparison group,
whether individuals in the treatment group should be matched with or without
replacement and whether matching should be "greedy" or "optimal". In this study, the
kernel weighting strategy was used to match individuals in the treatment group to the
comparison group. Also known as kernel matching, the kernel weighting strategy is
gradually gaining prominence in health research where other matching or weighting
strategies may not be viable options (Imbens, 2000; Dugoff et al., 2014). The kernel
weighting strategy involves assigning each individual in the treatment group a weight
of one (1). For each such individual, a weighted composite of individuals in the
comparison group is used to create a match based on their distance in the propensity
score from the individual in the treatment group. This distance is however done with a
range or bandwidth such that only comparison group observations outside the
bandwidth of "common support" are discarded (Garrido, 2014). Thus, kernel weighting
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strategy maximizes precision with the retention of most individuals sampled but
without worsening bias (as the weighting criteria gives greater weight to better
matches). Due to the methods used, kernel weighting lends itself to estimating the
average treatment on the treated (ATT) rather than the average treatment effect on the
entire study participants (ATE) (Garrido, 2014).
2.4.1.5 Assessment of balance of covariates after matching or weighting the
sample using the estimated propensity score
This step ensures that checks are made to evaluate how well the treatment group and
the comparison group are balanced in the matched/weighted samples. Methods used to
evaluate this balance assessment include;
a. Comparing standardised differences for each covariate before and
after matching/weighting: Although, there is no universal agreement
among researchers as to the threshold of the standardised difference
acceptable, a standardised difference of less than 10% in the
matched/weighted sample will indicate a negligible difference in the mean,
median or proportion of a covariate between treatment group and
comparison group (Normand et al., 2001; Austin, 2011b).
b. Balance diagnosis using graphs: With weighted data, the density
functions of continuous covariates in treatment and comparison groups can
be graphed together and compared subjectively (Austin, 2009). In addition,
a dot graph can be used to assess visually, the standardised difference.
c. Evaluation of ratio of variances: The ratio of variance of covariates in the
treated group to the variance of covariates in the comparison group should
optimally be one (1) if covariates are balance in the matched/weighted
sample. Rubin has however indicated that a ratio ::;0.5 or 2:2.0 are far too
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extreme to be accepted for propensity score-matching analysis (Rubin,
2001).
2.4.2 Analysis of data matched or weighted by the propensity score
After the deletion of individuals outside the "common support" and the method of
matching or weighting employed, the estimation of the treatment effect is done.
Treatment effect is referred to as the causal effect of a given treatment or intervention
on an outcome of interest (Austin, 2011b; Garrido, 2014). There are generally three
treatment effects of interest;
a. Average Treatment Effect on the entire sample (ATE): This refers to
the average effect, at population level, of moving an entire population from
untreated to treated (Austin, 20 11b).
b. Average Treatment Effect on the Treated (ATT or ATET): This refers
to the average effect of the treatment on an individual in the treatment group
(Austin, 20 11b).
c. Average Treatment Effect on the Untreated (ATU): This refers to the
average effect of the treatment on an individual in the untreated or
comparison group (Austin, 20 11b).
Although both ATE and ATT entails a statistical approach to defining causal effect
based on the Rubin causal model (Rubin, 1974), ATE is used mostly in randomised
control trial and regardless of the context of the research, PSM analysis is reserved for
estimating ATT (Austin, 2011b; Nduka et al., 2016b).
2.4.3 Post-estimation sensitivity analysis
From an epidemiological perspective, post-estimation sensitivity analysis in propensity
score-matching analysis is required as a tool to evaluate the extent to which a significant
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relationship found between a treatment and an outcome could be due to unobserved
confounding. These tests are used to quantify the strengths of any association between
hypothetical unobserved covariates and the exposure and outcome (Liu et al., 2013).
Thus, the sensitivity analysis aims to use hypothetical unobserved covariates to achieve
a true association between an exposure and an outcome with varying levels of
sensitivity parameters (Liu et al., 2013).
From a statistical approach, sensitivity analysis in PSM analysis uses a sensitivity
parameter to measure the difference in the odds of exposure for two study participants
with the same estimated propensity score but diverge on unobserved covariates (Liu et
al., 2013). The analysis yields results that estimate the smallest value of the sensitivity
parameter that will change the p-value of the "true" exposure-outcome association from
a significant level to a non-significant level (Liu et al., 2013). The Rosenbaum's
approach to sensitivity analysis is the most widely used method in matched analysis
including PSM analysis and this determines the threshold of the association between
unobserved covariates and the exposure and/or the threshold of the association between
unobserved covariates and the exposure that would render the determined test statistics
insignificant.
2.S Cardiovascular risk scoring systems
Cardiovascular disease affects most adults and is common in the general population.
Cardiovascular disease includes diseases like coronary heart disease, peripheral arterial
disease and aortic disease. Although the risk of CVDs increases with age, predicting
the risk of CVD in an individual can lead to interventions to reduce the occurrence of
future events. Cardiovascular risk scoring systems give an estimate of the probability
that an individual will develop CVD within a specified length of time. Cardiovascular
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risk scoring systems are very useful tools both to individual patients and to attending
clinicians in helping decide on when to initiate appropriate life-style modifications and
preventive medical treatment.
Several cardiovascular risk-scoring systems on individuals have been developed in
different study populations targeted at different ethnic groups. These include the
Framingham Risk Score, the SCORE CVD death risk score, the QRISK CVD risk
estimator, the Reynolds CVD risk score for women and for men, the ACC/ AHA pooled
cohort hard CVD risk calculator and the JBS3 risk calculator (Wilson, 2017). Others
are the MESA risk score, the China-PAR risk predictor, the Rama-EGAT risk score,
the WHO/ISH risk prediction chart and the D:A:D risk score. The most popular and
widely used of these CVD risk assessment scores is the FRS, which was modelled from
the Framingham Heart Study (Wilson, 2017). With the exception of the D:A:D 5-year
cardiovascular risk scoring system meant for the HIV -positive population, the rest of
the scoring systems are designed for the general population. However, with the HIV-
positive sub population, issues on the effect of HIV itself and the administration of ART
on the risk of CVD have raised appropriateness of the use of general CVD risk scores
in PLHIV. A commentary by D'Agostino (D'Agostino, 2012) reviewed the use of the
FRS and the D:A:D CVD risk equation in HIV -positive population and suggested that
whilst FRS can be used for general CVD risk assessment in PLHIV, more specific
assessment tools like the D:A:D must be developed for the HIV -positive population.
2.5.1 The Framingham 10-year general cardiovascular disease risk score
The Framingham risk score (FRS) is a sex specific cardiovascular risk scoring system
used in estimating the 10-year cardiovascular risk of an individual. First published in
1998 (Framingham Coronary Heart Disease Risk Score) (Wilson et al., 1998) with
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subsequent edited versions of it published in 2002 (ATP III hard CHD Risk Score)
(NCEP, 2002) and 2008 (General Framingham CVD risk score) (D'Agostino et al.,
2008), the FRS is the most popular and widely used CVD risk score. The FRS was
developed from the Framingham Heart study and it has been validated in different
populations/ethnic groups in the USA (D'Agostino et al., 2001). Although, there has
been studies with claims of improvement over the FRS, a study indicated little evidence
to back such improved prediction over the FRS (Tzoulaki et al., 2009). However, a
review of studies, which compared different CVD risk score tools, indicated that due to
the different study populations, reporting style and documentation it was difficult to be
conclusive on the superiority of any CVD predictive model (Siontis et al., 2012).
The current version of the FRS takes into consideration for each individual CVD risk
assessment the following parameters; age, total cholesterol, smoking status, HDL-
cholesterol, systolic blood pressure, treatment for hypertension or not and it is sex-
specific (D'Agostino et al., 2008). A modified version of the general FRS takes into
account a positive history of premature cardiovascular disease in a first degree relative
before the age of 55 years (for men) or 65 years (for women) (Genest et al., 2009).
2.5.2 The D:A:D 5-year cardiovascular disease risk score
The data Collection on Adverse Events of anti-HUV drugs (D:A:D) cardiovascular risk
scoring system was developed from the D:A:D study and published in 2010 (Friis-
Moller et al., 2010) and subsequently updated in 2016 (Friis-Moller et al., 2016). The
D:A:D risk score is a specific cardiovascular risk score developed from a cohort of
HIV -positive patients taking into consideration the type and duration of ARV s
administered. The DA:D study used a 5-year prediction model based on the fact that
models based on time-updated data may more accurately capture and predict
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individual's current risk. The full model takes into consideration age, sex, family history
of CVD, smoking status, diabetes status, total cholesterol level, HDL-cholesterollevel,
systolic blood pressure, CD4 T-cell count, years of exposure to a protease inhibitor
(lopinavir), years of exposure to a NRTI and exposure to abacavir or not (Friis-Moller
et al., 2016).
2.5.3 The WHO/ISH risk prediction charts
The World Health Organisation/International Society of Hypertension (WHO/ISH) risk
prediction chart is a cardiovascular risk scoring system tool developed for assessment
and prediction of cardiovascular risk in different populations (Mendis et al., 2007). It
is developed based on a 10-year modelling approach from the WHO comparative risk
assessment study and incorporates the factors age, sex, diabetes status, systolic blood
pressure, smoking stats and total serum cholesterol (Ezzati et al., 2003). With specific
charts designed specifically for non- Western countries where cohort data and resources
are believed to be unavailable, the WHO/ISH prediction charts uses information on the
relative risk of each risk factor, along with population level estimate of absolute risk to
develop a risk prediction chart which is regionally-based and thus can be used for
LMIC. The charts are divided into the 14 WHO epidemiological sub-regions; Africa
(AFR D and E), The Americas (AMR A, B and D), Eastern Mediterranean (EMR B
and D), Europe (EUR A, B and C), South-East Asia (SEAR B and D) and Western
pacific (WP A and B) (WHO, 2007).
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CHAPTER THREE
3.0 METHODS
3.1 Study design
A hospital-based cross sectional study was conducted at the HIV Clinic of the KBTH
in Accra, Ghana, from February 2016 to May 2016. All consenting HIV -positive
patients aged 18 years and above, non-pregnant (for females) and have been in
attendance at the HIV clinic for at least 6 months were eligible for recruitment into the
study. Patients excluded from the study were patients with prior diagnosis of
hypertension before HIV infection diagnosis or patients with sub-optimal adherence to
HIV clinic follow-up visits/ART medication or patients on hospitalisation /diagnosed
with AIDS. A simple random sampling technique was used to recruit potential study
participants into the study based on the routine clinic attendance. After going through
the eligibility criteria (inclusion/exclusion criteria), patients were recruited as study
participants. A questionnaire was administered to study participants to collect data on
socio-demographic characteristics, life-style characteristics and family history of
cardiovascular disease. Blood pressure and anthropometric measurements were made
and fasting blood samples taken for metabolic/biochemical parameters. Retrospective
chart review of clinical folders was done for baseline variables of each study
participant. HIV and ART-related data were extracted from the clinical folders of the
study participants. The prevalence of hypertension was estimated among the study
participants and the socio-demographic, life-style, anthropometric, metabolic and
HIV/ART -related factors associated with hypertension were determined by logistic
regression modelling. PSM-analysis was used to determine the ATT of ART on
hypertension and an estimation of the level of risk of CVD using cardiovascular risk
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score assessment tools (10-year Framingham risk score; 10-year WHO/ISH risk
prediction chart for fatal or non-fatal cardiovascular event and the 5-year D:A:D CVD
risk score) were made.
3.2 Study location
The study site was the HIV Clinic at the Korle-Bu Teaching Hospital. KBTH (Latitude
5 degrees, 36 minutes north; Longitude 0 degrees, 10 minutes east) is located at
Guggisberg Avenue (10 minutes' drive under normal traffic from the central business
centre of Accra) in the Ablekuma District of the Accra Metropolis in Greater Accra
Region of Ghana. Accra is the capital city of Ghana with an estimated population of 4
million, making it the eleventh largest metropolis in Africa. Accra is located in the
coastal savannah ecological zone, in the south-eastern end of Ghana with a land size of
about 173 krrr'. The average daily temperature is around 30°C (86°F) and receives an
annual rainfall of between 600 mm and 800 mm.
The HIV Clinic is located in the Fever's Unit under the Department of Medicine,
KBTH. Korle-bu Teaching Hospital was established on October 9, 1923 and it has
grown from an initial 200 bed capacity to about 2,000. It is currently the third largest
hospital in Africa and the leading national referral centre in Ghana. It has currently 17
clinical and diagnostic departments/units and an average daily attendance of 1,500
patients with about 250 daily patient admissions. It has three centres of excellence, the
National Cardiothoracic Centre, the National Reconstructive Plastic Surgery and Bums
Centre and the National Radiotherapy Centre. In addition, it is the premier tertiary
hospital in Ghana and serves as a research and training facility for students and
researchers of the University of Ghana.
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Currently the KBTH HIV Clinic has a 24-bed capacity and runs the largest cohort of
HIV patients in Ghana. Presently about 20,000 patients attend the clinic of which about
11,000 are on ART. The Korle Bu Teaching Hospital has been providing ART services
to PLHIV since December 2003. The hospital runs the HIV Clinic three days in a week
(Mondays, Wednesdays and Fridays) and operates an institutionalised electronic
database which stores all clinic visits as well as medications, laboratory investigations
and other patients' data. Currently the staff strength at the clinic is 5 clinicians, 30
nurses, 3 public health nurses, 8 pharmacists, 4 biomedical scientists, 6 volunteers, 8
counsellors and 5 data room staff. The fever's unit also houses cases of rabies and
tetanus (3-bed capacity).
3.3 Study population
The study population is the 20,000 HIV -positive patients who attend the HIV clinic and
the sampling frame is the electronic register of patients. The patient population is made
up of about 11,000 on ART with the rest on care. Each patient is scheduled to attend
the clinic once every three months (at a minimum) for both clinical assessment and
dispensing of medication (for patients on ARVs). Some patients have been on treatment
for the past 11 years.
At HIV clinic enrolment, patients routinely present with baseline clinical and laboratory
evaluations like physical examinations, full blood count, liver enzymes, serum
creatinine, and CD4+ T-cell count. Follow-ups are done every 3 months whilst
occasional viral load is requested for when treatment failure is suspected since it is
expensive and patients are unable to afford as a routine laboratory investigation. The
recommended baseline CD4 + T-cell count for initiating ART was a count of 250
cells/ul. or less and WHO clinical stage III/IV regardless ofCD4+ count. This changed
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later to 350 cells/ ~L or less in the 2011 guidelines and is presently pecked at 500 cells/
~L (NACP et aI., 2014). Patients receive comprehensive care at a minimum cost of
$3.00 or receive free care when registered under the National Health Insurance.
Irrespective of these requirements, patients not meeting the criteria are not excluded
from receiving continuous care. The care included medical review, laboratory
examinations, adherence counselling and general education on the disease and its
management. Adherence is reviewed at every clinic visit and documented and is
considered satisfactory when patients' self-report of compliance tallies with pill count.
3.4 Variables
Tables 6, 7, 8, 9 and 10 below summarises a list of socio-dernographic variables, family
history of CVD and lifestyle variables, blood pressure and anthropometric variables,
metabolic/biochemical variables, and ART /HIV -related and other variables measured
or extracted respectively.
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Table 6. Socio-demographic variables measured
Variable Definition Type of variable Scale of measurement
Age Current age (years) of study participant Explanatory l. Binary
• <40 years
• 2:40 years
2. Continuous
Sex Sex of study participant Explanatory Binary
• Female
• Male
Religion Religious affiliation of study participant Explanatory Categorical
• Christianity
• Moslem
• Others
Educational level Highest educational level attained by study Explanatory Categorical
participant • None
• Basic
• Secondary
• Tertiary/Professional
Marital status Marital status of study participant Explanatory Categorical
• Single
• Married/co-habiting
• Divorced/widowed/separated
Employment Employment status of study participant Explanatory Binary
• Unemployed
• Employed
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Table 7. Family history of cardiovascular disease and life-style variables measured
Variable Definition Type of variable Scale of measurement
Family history of Family history of hypertension or CVD in Explanatory Binary
hypertension/CVD parents or siblings • Present
• Absent
Tobacco smoking status Smoking of any form of tobacco including Explanatory Binary
cigarettes • Ever smoker
• None smoker
Alcohol drinking status Drinking of alcohol regularly Explanatory Binary
• Drinker
• Abstainer
Fruit consumption This a measure of how frequent study Explanatory Binary
participant consumes fruits • Never/rarely
• Most at times
Engagement in physical This a measure of how frequent study Explanatory Binary
activity participant engages in physical activities • Never/rarely
• Most at times
CVD=Cardiovascular disease
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Table 8. Blood pressure readings measured or extracted from clinical folder
Variable Definition Type of variable Scale of measurement
Hypertension Current blood pressure readings were measured and categorised Dependent Binary
status as per WHO and European Society of Hypertension/European • Hypertensive
Society of Cardiology recommendations (Whitworth, 2003; • Non-hypertensive
Mancia et al., 2013; WHO, 2013c). Hypertension was defined as
current systolic blood pressure (sBP) ~140 mmHg and/or
diastolic blood pressure (dBP) ~90 mmHg on two different days
and/or self-reported/medical record history of current
antihypertensive therapy.
Baseline systolic Systolic blood pressure readings as at enrollment into HIV clinic Explanatory Continuous
blood pressure or at initiation of ART
Baseline diastolic Diastolic blood pressure readings as at enrollment into HIV Explanatory Continuous
blood pressure clinic or at initiation of ART
dBP=Diastolic blood pressure; sBP=Systolic blood pressure; ART=Antiretroviral Therapy
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Table 9. Anthropometric variables measured
Variable Definition Type of variable Scale of measurement
Weight Current weight (in kg) of study participant Explanatory
Height ContinuousCurrent height (in meters) of study participant Explanatory
Waist circumference ContinuousCurrent waist circumference (in em) of study participant Explanatory
Hip circumference ContinuousCurrent hip circumference (in ern) of study participant Explanatory
Current body mass ContinuousCurrent BMI was calculated using the Quetelet index: Explanatory
index (BMI) Binaryweight divided by height squared (kg/nr') and classified
as (WHO, 1995): • <25.0 kg/rrr'
• <25.0 kg/m2=underweightlnormal • 225.0 kg/rrr'
• >25.0 kg/m2=overweight/obese
Baseline body mass Baseline BMI (at enrolment into HIV clinic or at
index (BMI) Explanatory Binaryinitiation of ART) was calculated using the Quetelet • <25.0 kg/m?
index: weight (as at HIV clinic enrolment or at initiation
of ART) divided by height squared (kg/rrr') and classified • 225.0 kg/rrr'
as (WHO, 1995):
• <25.0 kg/m2=underweight/normal
• >25.0 kg/m2=overweight/obese
Current abdominal Current abdominal obesity was defined as (WHO, 2012) Explanatory Binary
obesity • waist-to-hip ratio of20.85 for women and
(Waist-to-Hip ratio) • waist-to-hip ratio of20.90 for men • Abdominal obesity present
• Abdominal obesity absent
Current abdominal Current abdominal obesity was defined as (WHO, 2012) Explanatory Binary
obesity • waist circumference >88 for women and
(Waist circumference) • waist circumference> 102 for men • Abdominal obesity present
• Abdominal obesity absent
BMI=Body mass index; ART=Antiretroviral Therapy
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Table 10.Metabolic/biochemical variables measured
Type of
Variable Definition variable Scale of measurement
Fasting plasma Fasting plasma glucose was classified according to the American Explanatory Binary
glucose (FPG) Diabetes Association guidelines (ADA, 2012) as; • Elevated FPG
• Elevated FPG ~126.1 mg/dL (~7.0 mmol/L) • Normal FPG
• Normal FPG <126.1 mg/dL (>7.0 mmollL)
Estimated eGFR was estimated according to the CKD-EPI creatinine equation Explanatory Binary
glomerular (Levey et al., 2009) and classified; • Normal eGFR
filtration rate • Normal eGFR : ~60 minlmLl1.73 m2 • Reduced eGFR
(eGFR) • Reduced eGFR: <60 minlmLll. 73 m2
Total cholesterol Total cholesterol was classified using the National Cholesterol Explanatory Binary
Education Program, Adult Treatment Panel III (NCEP/ATP III) (NCEP, • Hypercholesterolemia
2002) • Normal total
• Hypercholesterolemia: Total cholesterol level ~5.17 mmol/L cholesterol
• Normal total cholesterol: Total cholesterol level <5.17 mmollL
HDL-cholesterol HDL-cholesterol level was classified using NCEPI ATP III (NCEP, Explanatory Binary
2002) • Abnormal HDL-C
• Abnormal HDL-C: HDL-cholesterollevel <1.03 mmol/L • Normal HDL-C
• Normal HDL-C: HDL-cholesterollevel > 1.03 mmollL
LDL-cholesterol LDL-cholesterol level was classified using NCEP/ATP III (NCEP, Explanatory Binary
2002) as • Elevated LDL-C
• Elevated LDL-C: LDL-cholesterollevel ~3.36 mmol/L • Normal LDL-C
• Normal LDL-C: LDL-cholesterollevel <3.36 mmol/L
Triglycerides Triglycerides level was classified using NCEP/ATP III (NCEP, 2002) Explanatory Binary
• Elevated triglycerides: Triglycerides level ~2.26 mmoliL • Elevated triglycerides
• Normal triglycerides: Triglycerides level <2.26 mmollL • Normal triglycerides
HDL-C=High-density lipoprotein cholesterol; LDL-C=Low-density lipoprotein cholesterol; CKD-EPI= Chronic Kidney Disease
Epidemiology Collaboration; eGFR=estimated glomerular filtration rate; NCEP/ATP III=National Cholesterol Education Program, Adult
Treatment Panel III; FPG=Fasting plasma glucose
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Table 11. HIV/ART-related variables measured or extracted from clinical folder
Variable Type ofDefinition variable
Current CD4+ T-cell Scale of measurementCurrent CD4+ T-cell count of study participant
count Explanatory Binary
• ::;350 cells/ul.
Nadir CD4+ T-cell Lowest ever CD4+ T-cell count of study participant • >350 cells/f!L
count Explanatory Binary
• ::;350 cells/ul.
Baseline CD4+ T-cell • >350 cells/ul.CD4+ T-cell count of study participant at HIV diagnosis/at Explanatory
count Binaryenrolment into HIV clinic or at initiation of ART
• ::;350 cells/ul.
HIV positive duration • >350 cells/ul.Time since HIV diagnosis and study participation (in years)
ART exposure Explanatory ContinuousART-exposed was defined as the administration of either two Explanatory Binary
NRTIs (or one NRTI and one NtRTI) + one NNRTI or 2 NRTls
(or one NRTI and one NtRTI) + one PI. • ART -exposed
Duration of ART Cumulative exposure to ART (in months) • ART-naive
Presence of Explanatory ContinuousAny other chronic diagnosed disease like diabetes, TB, hepatitis
co-morbid condition Explanatory BinaryB or C, chronic kidney disease, within the last 6 months as
documented in clinical folder • Present
• Absent
ART=Antiretroviral Therapy; NRTI=Nucleoside reverse transcriptase inhibitor; NtRTI=Nuc1eotide reverse transcriptase inhibitor;
NNRTI=Non-nucleoside reverse transcriptase inhibitor; PI=Protease inhibitor
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3.5 Sampling
3.5.1 Sample size calculation
A minimum sample size was calculated based on the estimation of a population
parameter for cross sectional studies (Charan and Biswas, 2013).
Sample size = CZl-aJ?ip Cl-p)
d2
Where
• ZI-oJ2= 1.96 at Type I error of 5% i.e. 95% confidence interval
• p = expected proportion of hypertension in PLHIV =25.6% (Ngala and
Fianko, 2014)
• d=absolute error = 5%
Sample size = 1.962 x 0.256 0-0.256)
0.052
The calculated initial minimum sample size was 293 study participants. Given that,
several characteristics of ART-exposed and ART-naive study participants were to be
matched on the propensity score, which will lead to deletion of study participants "off
support" a 5% adjustment was added to the initial minimum sample size calculated
(Nduka et aI., 2016b). The final minimum sample size calculated was 308. A total of
311 patients attending the HIV clinic at the KBTH were recruited as study participants
for this study.
3.5.2 Sampling procedure
3.5.2.1 Inclusion criteria
1. Both sexes.
2. Patients> 18 years of age.
3. Patients who have been enrolled into the HIV clinic for a minimum of 6
months and have had at least 3 clinic visits.
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3.5.2.2 Exclusion criteria
1. Patients diagnosed with hypertension before HIV -positive status diagnosis
or before enrolment into the HIV clinic.
2. Females who are pregnant and those who had given birth in the last 6
months as at the time of study
3. Patients with clinical AIDS or on hospitalisation
4. Patients with sub-optimal adherence to follow-up visits to HIV clinic
«95%). Adherence was measured using proportion of days covered (PDC)
(Ankrah et aI., 2015).
3.5.2.3 Selection of study participants
A simple random sampling technique was used for the selection of study participants
as follows. The sampling unit were individuals attending the HIV clinic. The clinic
operates 3 days in a week (Mondays, Wednesdays and Fridays) and attends to about
150 patients on each of these days and the sampling frame are the patients booked for
each clinic day.
The maximum cycle of a patient attending the HIV clinic is 3 months i.e. 12 clinic
weeks results in a cycle of 36 clinic days at a minimum. This means a patient is likely
to present to the clinic at least once every 36-clinic days. The sampling was done to
give every patient an equal chance of being represented; hence, the number of patients
sampled per clinic day was eight (8).
Computer generated random sequence of eight unique code numbers was generated
from the sampling frame for each clinic day for 40 clinic days and these were the
patients to be recruited into the study. The sampling frame was the list of patients
booked for a clinic day. Every patient in a particular day's list (sampling frame) was
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given a umque code number (extracted from folder number). Previously sampled
patients were excluded from subsequent sampling frame usmg date of last clinic
attendance. After recruitment, patients were taken through the consent process and after
satisfying the study selection criteria. Recruited patients who did not satisfy the
inclusion/exclusion criteria or who refused to take part in the study on any particular
study day were not replaced but the enrolment procedure continued until the sample
size estimated was reached. A total of 40 clinic days were used for the recruitment of
study participants.
3.6 Data collection techniques
3.6.1 Consenting process and participation in study
In this study, randomly selected potential study participants were recruited whilst
waiting to see the clinician. Each recruited study participant was privately informed
about the objectives and purpose of the study, risks and benefits of participating in the
study and that his/her participation will be purely voluntary and there will be no
negative effect for not consenting to participate in the study. After the consenting
process, each participant was administered the questionnaire, blood pressure readings
taken, anthropometric measurements done and asked to come back the following
morning fasted for blood sampling. Follow-up telephone calls were made to recruited
patients and they were reminded to fast before coming for blood sampling the following
day.
3.6.2 Data collection from study participants
A questionnaire adopted from WHO STEPwise approach to chronic disease risk-factor
surveillance (WHO, 2008a) was modified and used for the collection of study
participants' data. In addition, other relevant clinical characteristics were obtained from
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the medical history record (clinical folder) of the study participants. Information
obtained from the study participants were classified under the following groupings:
1. Socio-demographic and life-style characteristics
2. Blood pressure, anthropometric and biochemical measurements
3. HIV /ART -related and other data extracted from clinical folders
3.6.2.1 Socio-demographic and life-style characteristics
Data on age, sex, employment, educational level attained and marital status were
obtained from the study participants through an interviewer-administered
questionnaire.
Life-style characteristics in terms of alcohol consumption, smoking status, fruit
consumption and engagement in physical activity were obtained from the study
participants through interviewer-administered questionnaire. Data on family history of
cardiovascular disease were also obtained.
3.6.2.2 Blood pressure, anthropometric and biochemical measurements
3.6.2.2.1 Blood pressure measurement
Blood pressure readings were taken by a trained nurse in the outpatient clinic with a
new calibrated analogue mercury sphygmomanometer (Tycoo'", USA) having an
adjustable cuff. Blood pressure measurements were made with study participants
seated in an upright position and relaxed. On the day of enrolment, study participants
were made to sit for 10 minutes or more before blood pressure readings were made. In
accordance with the WHO STEPwise approach to chronic disease risk-factor
surveillance protocol (WHO, 2008a), two measurements were taken 3 minutes apart on
alternating arms and the average of these two readings estimated and used in the
analysis. This is in accordance with WHO recommendation to use the average of blood
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pressure readings at one visit in risk factor surveys (WHO, 2008a). An additional blood
pressure measurement was made on each study participant four weeks after the first
measurement using the same procedure.
3.6.2.2.2 Weight measurement
The body weight of study participants was measured using a calibrated digital Seca'"
813 electronic weight measuring scale (Seca, Hamburg, Germany). The scale was
placed on an even surface and study participants were made to stand in the centre of the
scale's platform bare-footed with their weight distributed evenly to both feet. Two
measurements were made to the nearest 0.1 kg and the average estimated.
3.6.2.2.3 Height measurement
The height of study participants was measured usmg a wall mounted Seca" 213
stadiometer (Seca, Hamburg, Germany). Study participants were asked to remove their
footwear and cap and made to stand upright with their back to the height rule and head
in a Frankfurt horizontal plane. Readings were made in a position to avoid parallax
error. Two measurements were made to the nearest 0.1cm and the average estimated.
3.6.2.2.4 Waist circumference measurement
The waist circumferences of study participants were measured using an inelastic but
flexible 203 em Seca" standard tape measure (Seca, Hamburg, Germany). Study
participants were made to stand with their feet close together and their weight equally
distributed to each leg. Measurements were done at a level midway between the lower
rib margin and iliac crest with the tape all around the body in horizontal position. Two
measurements were made to the nearest O.lcm.and the average estimated.
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3.6.2.2.5 Hip circumference measurement
Hip circumference of study participants was measured with a 203 ern Seca" standard
tape measure (Seca, Hamburg, Germany). Study participants were made to stand with
their feet close together and their weight equally distributed to each leg and hip
circumference measured as the maximal circumference over the buttocks. Two
measurements were made to the nearest O.lcm and the average estimated.
3.6.2.2.6 Sample collection and preparation for biochemical measurements
After an overnight fast (minimum of 12 hours), 7 mL of blood was drawn from the
median cubital vein on the anterior forearm of each study participant and aliquot into
plain tube, fluoride oxalate tube and gel separator tube (BD Vacutainer®). Blood
samples in the plain tube were gently mixed for 5 minutes and analysed for CD4+ T-
cell count. Blood samples in the fluoride oxalate tubes were gently mixed for 5 minutes
and centrifuged at 2500g for 10 minutes at room temperature. The plasma was separated
and analysed for fasting plasma glucose. Blood samples in the gel separator tubes were
allowed to clot for 30 minutes and then centrifuged at 2500g for 10 minutes, at room
temperature. The serum was then separated and analysed for creatinine concentration,
total cholesterol concentration, HDL-cholesterol concentration and triglycerides
concentrati on.
3.6.2.2.7 CD4+ T-cell count
CD4+ T-cell count was determined by flow cytometry using anti-CD4, anti-CD14 and
anti-CD 15 monoclonal antibodies with the Becton Dickinson FACSCount® analyser
(BD Biosciences, San Jose, CA 95131, USA) following the manufacturer's protocol.
CD4+ T-cell count was determined with BD FACSCountTM CD4/CD3 reagent kits
(BD Bioscience, San Jose, USA), using BD FACSCount System analyzer (BECTON
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DICKINSON, USA). The FACSCount® system is a single-platform instrument
designed specifically for enumerating absolute CD4+ T-cell count in no-lyse, no-wash
whole blood. The flow cytometry assay works on the principle of scattering of light due
to different sizes, granularity of the cells passing through the laser beam, and also by
the fluorescence emitted by the cells after staining with the specific monoclonal
antibodies to cell surface markers that are tagged with fluorescence dyes. A volume of
20 ~L of MultiTEST CD3/CDS/CD45/CD4 reconstituted working reagent was added
to 50 ~L whole blood, mixed and incubated for 15 minutes at room temperature. FACS
lysing solution (450 uL) was then added, mixed gently, incubated at room temperature
and then readings made with the auto analyser.
3.6.2.2.8 Fasting plasma glucose
Fasting plasma glucose concentration was determined by the glucose oxidase-
peroxidase colorimetric method (GOD-POD) using Randox RX Monza'" chemistry
auto analyser (Rando x laboratories Ltd.). A volume of 50 ~L of plasma was pipetted
into the sample well and assayed for glucose concentration. The principle behind the
method involves the oxidation of glucose to gluconate and hydrogen peroxide (H202)
by glucose oxidase (GOD). The H202 produced is then coupled with phenol and 4-
aminoantipyrine (4-AP) in the presence of peroxidase (POD) to yield the coloured
complex quinoneimine, which is measured at 505 nm. The intensity of the coloured
complex formed is directly proportional to the concentration of glucose in the sample.
Glucose oxidase
~-D-Glucose + 02+ H20 --------+Gluconate + H202
Peroxidase
2H202 + Phenol + 4-aminoantipyrine -- -+ Quinoneimine + 4H20
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3.6.2.2.9 Total cholesterol
Serum concentration of total cholesterol was measured by the enzymatic endpoint
method using Randox RX Monza" chemistry auto analyser (Randox laboratories Ltd.).
A volume of 50 ul. of serum was pipetted into the sample well and assayed for total
cholesterol concentration. The enzymatic endpoint method involves the conversion of
esterified cholesterol to cholesterol by the enzyme, cholesterol ester hydrolase. The
resulting cholesterol is then acted upon cholesterol oxidase to yield cholest-4-en-3-one
and H202. The H202 produced is then coupled with 4-aminoantipyrine (4-AP) in the
presence of peroxidase (POD) to yield the coloured complex quinoneimine, which is
measured at 505 nm. The intensity of the coloured complex formed is directly
proportional to the concentration of total cholesterol in the sample.
Cholesterol ester hydrolase
Cholesteryl ester + H20 --------------. Cholesterol + fatty acid
Cholesterol oxidase
Cholesterol + 02 Cholest-4-en-3-one + H202
Peroxidase
2H202 + Phenol + 4-aminoantipyrine ---- ...... Quinoneimine + 4H20
3.6.2.2.10 High-density lipoprotein cholesterol
High-density lipoprotein cholesterol was measured by the direct enzymatic colorimetric
method using Randox RX Monza" chemistry auto analyser (Randox laboratories Ltd.).
A volume of 50 ul. of serum was pipetted into the sample well and assayed for high-
density lipoprotein cholesterol concentration. The principle behind the method involves
the reaction of apoB containing lipoprotein in the sample with a blocking agent, a
detergent. This renders them non-reactive and thus the non-HDL lipoprotein LDL, very
low density lipoprotein and chylomicrons are inhibited from reacting with the
enzymatic cholesterol reagent. This ensures HDL-cholesterol alone is measured in the
reaction assay. The enzymatic endpoint method involves the conversion of esterified
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HDL-cholesterol to unesterified HDL-cholesterol by the enzyme PEG cholesterol
esterase. The resulting unesterified HDL-cholesterol is acted upon cholesterol oxidase
to yield cholestone and H202. The H202 produced is then coupled with 4-
amino antipyrine (4-AP) in the presence of peroxidase (POD) to yield the coloured
complex quinoneimine, which is measured at 600 nrn. The intensity of the coloured
complex formed is directly proportional to the concentration of total cholesterol in the
sample.
PEG Cholesterol
esterase
HDL-cholesteryl ester + H20 ----+ HDL-unesterified cholesterol + fatty acid
Cholesterol oxidase
Unesterified cholesterol + 02 PEG ---.
Peroxidase
2H202 + Phenol + 4-arninoantipyrine _______ ___..Quinoneimine+ 4H20
3.6.2.2.11 Triglycerides
Triglycerides concentration was measured by the GPO-PAP method using Randox RX
Monza'" chemistry auto analyser (Randox laboratories Ltd.). A volume of 50 ul. of
serum was pipetted into the sample well and assayed for triglycerides concentration.
Triglycerides concentration was determined after enzymatic hydrolysis with lipases to
yield glycerol and fatty acids. The glycerol is phosphorylated to glycerol-3-phospahte
by coupled reaction with adenosine triphosphate (ATP) (converted to adenosine
diphosphate-ADP) catalysed by glycerol kinase. Glycerol-3-phosphate is then
oxygenated to dihydroxyacetone phosphate and H202 by the enzyme glycerol-3-
phosphate oxidase. The H202 produced is then coupled with 4-aminoantipyrine (4-AP)
and 4-chlorophenol in the presence of peroxidase (POD) to yield the coloured complex
quinoneimine, which is measured at 505 lID1. The intensity of the coloured complex
formed is directly proportional to the concentration of total triglycerides in the sample.
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Lipase
Triglycerides + H20 ---------. Glycerol + fatty acids
Glycerol kinase
Glycerol + ATP ---------.Glycerol -3-phosphate + ADP
Glycerol-3-
phosphate oxidase
Glycerol -3-phosphate + 02 Dihydroxyacetone phosphate + H202
Peroxidase
2H202 + 4-chlorophenol + 4-aminoantipyrine--_-.Quinoneimine + 4H20 +HCI
3.6.2.2.12 Low-density lipoprotein cholesterol
LDL-C serum concentration was calculated using the Friedewald equation as follows:
[LDL-C] [Total Cholesterol]-[HDL-C] ([Triglycerides]/2.2) where all
concentrations are in mmollL (Friedewald et aI., 1972).
3.6.2.2.13 Creatinine
Serum creatinine concentration was measured by the enzymatic method using Randox
RX Monza'" chemistry auto analyser (Randox laboratories Ltd.). A volume of 50 ul. of
serum was pipetted into the sample well and assayed for creatinine concentration.
Creatinine is converted to creatine by the enzyme creatininase. Creatine is then
converted to sarcosine by creatine amidinohydrolase (creatinase). The sarcosine formed
is then oxidised to produce H202, which is coupled with 4-aminoantipyrine (4-AP) and
N-Ethyl-N-(2-Hydroxy-3-sulfopropyl)m-toluidine (TOOS) in the presence of
peroxidase (POD) to yield the coloured complex quinoneimine. The coloured complex
is measured at 550 nm and the intensity of it is directly proportional to the concentration
of creatinine in the sample.
Creatin inase
Creatinine + H20 ------------. Creatine
Creatinase
----------.varcosine + Urea
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Sarcosine oxidase
Sarcosine + 02 -.Glycine + HCHO + H202
Peroxidase
2H202 + TOOS + 4-aminoantipyrine -------__.Quinoneimine + 4H20
3.6.2.3 HIV/ART-related and other data extracted from clinical folders
ART/HIV -related data were extracted from patients' folders with the assistance of a
trained in-house pharmacist. Data extracted included blood pressure measurements at
enrolment into HIV clinic or at initiation of ART (baseline systolic and diastolic blood
pressures) and body mass index at enrolment into HIV clinic or at initiation of ART
(baseline BMI). Others were duration of HIV infection, type of HIV infection and WHO
HIV stage at diagnosis, date of ART initiation, ARVs administered and duration,
presence of co-morbidities, current administration of antihypertensive treatment, CD4+
T-cell count at HIV diagnosis or at enrolment initiation of ART (baseline CD4 T-cell
count) and Nadir CD4+ T-cell count.
Medication adherence level of patients on ART was obtained from the pharmacy
records comparing the appointed dates for ART refill and the actual reporting dates of
refill. The sum of default days as a proportion of days covered (PDC) was calculated
and used as a marker oflevel of medication adherence (Ankrah et al., 2015).
3.6.3 Quality control
A one-day training session was held with all study personnel on data collection with
emphasis on data quality and strict accountability system. A pre-test of the study
questionnaire was done with 20 volunteers at the diabetes clinic of the KBTH and all
challenges with the wording of specific questions addressed appropriately. A database
was developed in Microsoft Access'" and was used to double-check all logic
discrepancies to validate the data. All laboratory analyses were conducted to follow
standard operating procedures and all equipment calibrated according to manufacturers'
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specification. Daily controls were run during laboratory assay of study participants'
blood samples.
3.7 Data management and statistical analysis
3.7.1 Data management
Data were double entered into Microsoft Access'" database. Standardized queries were
used to conduct range and logic checks, and discrepant entries were rectified after a
review of collected data. Data management techniques used involved sorting, merging,
appending and data reorganization to suit the statistical routines used. After double data
entry, comparison of the two datasets was done and tagging, or dropping duplicate
observation effected. String variables were encoded into numeric and vice versa.
Verification of truth of claim was done between information obtained from
questionnaire and institutional database. In addition, required data was converted from
wide to long form and vice versa and categorical variables recoded as needed.
3.7.2 Statistical analysis
Stata® 14 software was used to analyse the data. Continuous variables were reported
as mean ± SD or median with interquartile range if not normally distributed. Continuous
variables were modelled in class or after log transformation. Categorical variables were
reported as percentages and proportions and were compared using Pearson chi-square
statistics. The statistical analyses conducted in this study were in four parts:
1. Descriptive analysis to determine the prevalence of hypertension in entire
study participants and in different sub-groups
2. Propensity score-matching analysis to estimate average treatment effect (on
the treated) of ART exposure on hypertension and blood pressure values
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3. Logistic regression modelling to determine factors associated with
hypertension in study participants
4. Analysis to estimate risk of cardiovascular disease in study participants
using the 1O-year general Framingham cardiovascular risk score, the 5-year
D:A:D cardiovascular risk score and the 1O-year WHO/ISH risk prediction
chart
3.7.2.1 Descriptive analyses to determine prevalence of hypertension
Variables used in estimating prevalence of hypertension in entire study participants and
in sub-groups were; current hypertension status, age, sex and ART -exposure.
Prevalence of hypertension was calculated for entire study population and sub-groups
of age «40 years and ~40 years), sex (male and females) and ART exposure (ART-
naive and ART -exposed). The z-test of proportionality was used to test for homogeneity
of proportion of hypertensive subjects across the sub-groups of age, sex and ART
exposure.
3.7.2.2 Logistic regression modelling to determine factors associated with
hypertension
The variables used in determining factors associated with hypertension in the regression
analysis were current hypertension status (outcome variable), ART -exposure (main
exposure variable), age, sex, religion, employment status, marital status, educational
level, family history of hypertension/Cv'D, smoking status, alcohol, physical activity
and fruit intake. Other covariates were HIV infection duration, duration of ART
administration, HIV -type, presence of co-morbidity, current BMI, abdominal obesity,
fasting plasma glucose, total cholesterol level, HDL-C level, LDL-C level, triglycerides
level, eGFR, current CD4+ T cell count and nadir CD4+ T-cell count.
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A logistic regression analysis was carried out to determine the factors associated with
hypertension. The preliminary univariate analysis was designed to determine the
associated factors as grouped under socio-demographic and life-style factors,
anthropometric and metabolic/biochemical factors and HIV /ART -related factors.
Thereafter, a multiple logistic regression model was generated using the purposefuL
selection of covariates method (Hosmer and Lemeshow, 2000; Bursae et al., 2008).
The purposeful selection of variables (deleting, refitting, and verifying) was done as
follows (Bursae et al., 2008):
1. A preliminary univariate analysis was done in and all variables found to be
associated with the outcome at a p-value of <0.20 were selected to be part
of the initial multiple regression analysis.
2. An initial multiple logistic regression analysis was then run with selected
variables from the univariate analysis
3. This was followed by an iterative process of purposeful removal of
covariates in the multiple regression analysis based on their non-
significance at p>O.1
4. Confounders were then purposefully removed from the remaining list of
variables if they did not change parameter estimate by at least 15%
5. At the end of these iterative processes, only significant covariates and
confounders were in the multiple regression model.
6. The multiple regression analysis model (containing the significant
covariates and confounders) was then run again to include one at a time all
variables initially not included in the iterative process (deleted during the
univariate analysis because they were non-significant at p>0.20).
Significance of these re-entered variables was tested as covariates and as
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confounders as previously done in 3 and 4 above, but this time on only the
re-entered variables.
7. At the end of this final step, the multiple logistic regression analysis
contains preliminary main effects model.
The performance of the final model was assessed on "calibration" using the Hosmer-
Lemeshow goodness-of-fit test statistic. This was used to test the level of agreement
between predicted probabilities and the 'true probabilities' using the mean of the
observed outcomes within predefined groups of study participants (Hosmer and
Lemeshow, 2000). This is done after the observations has been sorted (according to
their expected probability) and partitioned into 10 groups of equal sizes. The C-
goodness-of-fit statistic gives a large "H" value with a corresponding small (Hosmer-
Lemeshow X2) p-value if there is a significant difference between the observed outcome
and the predicted outcome and this indicates evidence of lack of fit of the variables
involved in the final model.
The performance of the final model was also assessed on discrimination using the
Receiver Operating Characteristics (ROC) area under the curve (AUC). This was used
to verify whether a set of predictor variables in the final model were able to distinguish
hypertension from non-hypertension and the AUC refers to the probability that a
hypertensive person has a higher predicted probability than those who were non-
hypertensive using the variables involved in the final model. The discrimination of the
variables was considered; perfect if AUC=l.OO, good if AUC is 0.81-0.99, moderate if
AUC is 0.60-0.80 and poor if AUC < 0.6 (Dodoo et al., 2009).
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3.7.2.3 Propensity score-matching analysis to estimate average treatment
effect of ART on hypertension and blood pressure values
Variables used in estimating ATT of ART on hypertension were; current hypertension
status (outcome variable) and ART exposure (treatment variable). The covariates used
were age, sex, religion, employment status, marital status, educational level, family
history of hypertensionJCVD, smoking status, alcohol intake, baseline BMI, physical
activity, fruit intake, HIV infection duration, HIV -type, baseline sBP, baseline dBP,
baseline CD4 T-cell count, administration of antihypertensive medication and presence
of co-morbidity. A logistic regression was used (with ART exposure as the outcome
variable and covariates/confounders as exploratory variables) to estimate a propensity
score for each study participant. The balance of the estimated propensity scores across
the treatment group (ART-exposed) and the comparison group (ART-naive) for
common support was checked visually by a box plot and objectively by Student's t-test.
Balance of covariates across treatment group and comparison group was checked using
a standardised difference of not more than 10%. This iterative process was repeated
several times (deleting, re-categorisation and inclusion of interaction terms of
covariates) until the estimated propensity scores were balanced across treatment and
comparison groups and the entered covariates also balanced across treatment and
comparison groups within blocks of the estimated propensity scores.
The average treatment effect on the treated was estimated using the kernel matching or
weighting strategy with a bandwidth of 0.06. Thereafter, standard errors were
bootstrapped with 150 replications. The balance of covariates after the kernel weighting
strategy were checked by comparing the standardised differences before and after
matching/weighting (achieving <10% standardised difference), evaluation of ratio of
variances (between 0.5 and 2.0) and graphically using a dot graph. Post-estimation
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analysis for the extent of influence of unobserved covariates (sensitivity test) was done
using Rosenbaum bounds (the rbounds syntax in Stata®).
3.7.2.4 Analysis to estimate cardiovascular disease risk scores
The variables used in estimating the cardiovascular risk scores were age, sex, current
systolic blood pressure, FPG level/diagnosis of diabetes, total cholesterol level HDL-C
level, smoking status, family history of CVD, duration on indinavir, duration on
lopinavir and administration of abacavir. Analysis of data was done to estimate the 10-
year general Framingham cardiovascular risk score (FRS), the 5-year D:A:D
cardiovascular risk score and the 10-year WHO/ISH cardiovascular risk prediction
score of study participants. The FRSs were classified as low risk «10%), moderate risk
(10% to <20%) and high risk G::: 20%) (Genest et al., 2009; Anderson et al., 2013). The
D:A:D CVD risk scores were classified as low risk «1 %), moderate risk (l% to <5%)
and high/very high risk (2:5%) (Friis-Moller et al., 2016). The WHO/ISH cardiovascular
risk prediction scores were classified as low risk «10%), moderate risk (10% to <20%)
and high risk (2:20%). Comparisons of the cardiovascular risk scoring systems were
determined using Cohen's Kappa coefficient (K) with 95% confidence interval. Kappa
was interpreted as perfect agreement (>0.80), substantial agreement (0.61-0.80),
moderate agreement (0.41-0.60), fair agreement (0.21-0AO) and poor agreement
(:S0.20) (Edward et al., 2013; Mashinya et al., 2015). For the comparison of the D:A:D
5-year score with the FRS and the WHO/ISH scores, it was assumed that the 5-year
D:A:D prediction was constant over a 10 year period.
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3.8 Ethical consideration
3.8.1 Ethical clearance from Institutional Review Board
The proposal was submitted to the Ethical and Protocol Review Committee of the
College of Health Sciences, University of Ghana and was given ethical clearance
[Protocol Identification Number: MS-Et/M.3-P 4.4/2015-2016] (Appendix IV).
3.8.2 Consent from HIV clinic
Permission was obtained from the clinician-in-charge of the Fevers Unit at the KBTH
giving assurance of data protection and anonymity of patients' medical records.
3.8.3 Informed consent from study participants
Informed consent was sought and obtained from each individual study participant
informing him/her on the aim and purpose of the study, risks and benefits of
participating in the study (Appendix II and III).
3.8.4 Data safety
Patients' data were de-identified during data capture, entry, analysis and storage by
ensuring only the study codes were used consistently throughout the project cycle.
Research assistants and laboratory personnel for data collection were staff of the HIV
Clinic. Apart from the research team and the HIV Clinic staff, data were not accessible
to any other person. Each study participant's results were relayed to the attending
physician for appropriate care and health education.
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CHAPTER FOUR
4.0 RESULTS
4.1 Description of study participants
4.1.1 Socio-demographic and life-style characteristics of study participants
A total of 311 PLHIV were recruited as study participants. This was made up of 76.2%
females (Table 12). Most of the study participants were 40 years and above (73.0%,
n=227) (Table 12). Majority of the study participants do not smoke tobacco (95.8%,
n=298) or drink alcohol (68.5%, n=213) (Table 12). Presence of hypertension or other
cardiovascular disease in first-degree family members (parents/siblings) was absent in
over eighty percent of the study participants (Table 12).
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Table 12. Socio-demographic and life-style characteristics of study participants
Frequency
Characteristics N=311
n %
Age, median (Interquartile range) (years) 44.0 (39.0-51.0)
Age group
<40 years 84 (27.0)
>40 years 227 (73.0)
Sex
Female 237 (76.2)
Male 74 (23.8)
Highest educational level attained
None 44 (14.2)
Basic 163 (52.4)
Secondary 95 (30.5)
Tertiary/Professional 9 (2.9)
Marital status
Single 57 (18.3)
Married/Co-habiting 134 (43.1)
Widowed/Divorced/Separated 120 (38.6)
Employment status
Unemployed 38 (12.2)
Employed 273 (87.8)
Family history of hypertension/cardiovascular disease
Present 39 (12.5)
Absent 272 (87.5)
Tobacco smoking status
Ever smoker 13 (4.2)
Never smoker 298 (95.8)
Alcohol drinking status
Ever drinker 98 (31.5)
Abstainer 213 (68.5)
Fruit intake
RarelNever 99 (31.8)
Most at times 212 (68.2)
Engagement in physical activity/exercising
RarelNever 216 (69.5)
Most at times 95 (30.5)
IQR=Interquartile range; N=Total number of study participants; n=Number of
respondents per characteristic
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4.1.2 Blood pressure levels, anthropometric and biochemical indices of study
participants
A total of 114 study participants (36.7%) were hypertensive with their median current
systolic and diastolic blood pressure values being 148.5 mmHg (IQR, 142.0-163.0) and
95.0 mmHg (IQR, 90.0-101.3) respectively (Table 13). The median of their baseline
systolic and diastolic blood pressure values were 132.0 mmHg (IQR, 125.8-137.0) and
81.0 mmHg (IQR, 75.0-86.3) respectively. Ten percent (10%) of the study participants
had elevated fasting plasma glucose of ~7.00 mmollL, whilst 15.4% had reduced
estimated glomerular filtration rate of <60 min/mLl1. 73m2 (Table 13).
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Table 13. Blood pressure levels, anthropometric and biochemical indices of study
participants
Frequency
Characteristics N=311
n (%)
Current systolic blood pressure (sBP), median (IQR), mmHg 130 (113 -144 )
Current diastolic blood pressure (dBP), median (IQR), mmHg 80 (70-91)
Current blood pressure category
Non-hypertensive 197 (63.3)
Hypertensive 114 (36.7)
Current body mass index
<25.0 (kg/rrr') 189(60.8)
2:25.0 (kg/rrr') 122 (39.2)
Abdominal obesity (Waist-to-hip ratio)
Absent (WHR <0.85 for women; WHR <0.90 for men) 196 (63.0)
Present (WHR 2:0.85 for women; WHR 2:0.90 for men) 115 (37.0)
Abdominal obesity (Waist circumference)
Absent (WC :S88 for women; WC :S102 for men) 254 (81.7)
Present (WC >88 for women; WC > 102 for men) 57 (18.3)
Fasting plasma glucose
Normal FPG (FPG <7.0 mmollL) 280 (90.0)
Elevated FPG (FPG 2:7.0 mmol/L) 31(10.0)
Total cholesterol
Normal total cholesterol (TC <5.17 mmol/L) 148 (47.6)
Hypercholesterolemia (TC >5.17 ml11oI/L) 163 (52.4)
High-density lipoprotein cholesterol
Normal (HDL-C 2:1.03 mmol/L) 280 (90.0)
Abnormal (HDL-C <1.03 mmollL) 31(10.0)
Low-density lipoprotein cholesterol
Normal (LDL-C <3.36 mmol/L) 195 (62.7)
Elevated (LDL-C 2:3.36 mmollL) 116 (37.3)
Triglycerides
Normal (TG <2.26 mmollL) 300 (96.5)
Elevated (TG 2:2.26 mmol/L) 11 (3.5)
Estimated glomerular filtration rate
Normal eGFR (eGFR 2:60 minlmLl1.73 m') 263 (84.6)
Reduced eGFR (eGFR <60 minlmLl1.73 rrr') 48 (15.4)
dBP=Diastolic blood pressure; eGFR=Estimated glomerular filtration rate;
FPG=Fasting plasma glucose; HDL-C=High-density lipoprotein cholesterol;
IQR =Interquartile range; LDL-C=Low-density lipoprotein cholesterol; N=Total
number of study participants; n=Number of respondents per characteristic;
sBP=Systolic blood pressure; TC=Total cholesterol; TG=Triglycerides; WC=Waist
circumference; WHR=Waist-to-hip ratio
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4.1.3 HIV/ART related characteristics of study participants
Most of the study participants (73.6%) had current CD4+ T-cell count of>350 cells/uL
(Table 14). The median duration of HIV-infection for the study participants was 7.9
years and the median duration of ART administration (for ART-exposed group) was
6.8 years. Majority of the study participants were on ART (81.0%) of which the
administration of either AZT/3TCINVP or AZT/3TC/EFV constitutes over fifty percent
(Table 14).
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Table 14. HIV/ART-related characteristics of study participants.
Frequency
Characteristics N=311
n(%)
Baseline CD4+ T-cell count
::;350 cells/ul. 176 (56.6)
>350 cells/ul. 135 (43.4)
Current CD4+ T-cell count
::;350 cells/ul. 82 (26.4)
>350 cells/ul. 229 (73.6)
Nadir CD4+ T-cell count
::;350 cells/uL 220 (70.7)
>350 cells/ul. 91(29.3)
Duration since HIV-positive diagnosis, median (IQR) (years) 7.9 (4.5-10.6)
HIV-type
Type I only 228 (73.3)
Type IIonly 7 (2.3)
Mixed (Type I and II) 76 (24.4)
Presence of co-morbidities (excluding hypertension)
Absent 243 (78.1)
Present 68 (21.9)
ART exposure status
ART-naive 59 (19.0)
ART -exposed 252 (81.0)
ARV composition of ART administered (N=252)
TDF /FTC/EFV 8 (3.2)
TDF/3TCINVP 13 (5.2)
TDF/3TC/EFV 57 (22.6)
TDF/3TC/LPV-r 32(12.7)
TDF/3TC/ATV-r 3 (1.2)
AZT 13TCINVP 58 (23.0)
AZT /3TC/EFV 81 (32.1)
Cumulative exposure to ART, median (IQR) (years) (N=252) 6.8 (5.9-7.5)
3TC=Lamivudine; ARV=Anti-retrovirals; ATV -r=Ritonavir boosted atazanavir;
AZT=Zidovudine; EFV=Efavirenz; FTC=Emtricitabine; ART=Antiretroviral Therapy;
IQR=Interquartile range; LPV -r=Ritonavir boosted lopinavir; N=Total number of study
participants; n=Number of respondents per characteristic; NNRTI=Non-nucleoside
reverse transcriptase inhibitor; NVP=Nevirapine; PI=Protease inhibitor;
TDF=Tenofovir; WHO=World Health Organisation
4.2 Prevalence of hypertension in study participants
The overall prevalence of hypertension in the 311 study participants was 36.7% (95%
CI, 31.3-42.3) (Table 15). The prevalence of hypertension in study participants aged
2':40 years (40.5%, [95% CI, 34.1-46.9]) was significantly higher compared with the
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prevalence in study participants aged <40 years (26.2%, [95% CI, 16.8-35.6]). The
prevalence of hypertension in ART-exposed study participants (41.3% [95% CI, 35.2-
47.3]) was significantly higher compared with the ART-naive study participants
(16.9%, [95% CI, 7.4-26.5]) (p<O.OOI) (Table 15). There was no statistical difference
in the prevalence of hypertension between male and female study participants.
Table 15. Prevalence of hypertension in study participants
Total Number of Prevalence
Group number (N) hypertensives (n) [95% CI] p-value
All study participants 311 114 36.7 [31.3-42.3]
Age group
<40 years 84 22 26.2 [16.8-35.6] 0.020
2:40 years 227 92 40.5 [34.1-46.9]
Sex
Male 74 30 40.5 [29.4-51.7] 0.427
Female 237 84 35.4 [29.4-41.5]
ART exposure
ART-naive 59 10 16.9 [7.4-26.5] <0.001
ART -exposed 252 104 41.3 [35.2-47.3]
CI=Confidence interval; ART= Anti-retroviral Therapy
4.3 Logistic regression mode11ing of factors associated with hypertension
in study participants
4.3.1 Univariate analysis of factors associated with hypertension
Analysis of Table 16 indicates that the median age of study participants who were
hypertensive (49.0 years; IQR, 40.8-57.0) was significantly higher than that for the non-
hypertensive (42.0 years; IQR, 37.0-48.0). Exercising, employment status and presence
of family history of cardiovascular disease were associated with hypertension in the
univariate analysis (Table 16). Table 17 shows that current body mass index of2:25.0
kg/nr' and abdominal obesity (waist circumference) were both associated with
increased odds of hypertension in the univariate analysis. Of the biochemical indices
measured, elevated levels of total cholesterol and LDL-cholesterol and reduced levels
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of estimated glomerular filtration rate were associated with hypertension in the
univariate analysis (Table 17). HIV/ART-related factors associated with hypertension
in the univariate analysis were exposure to ART, duration of both HIV infection and
ART exposure (Table 18).
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Table 16. Univariate analysis of socio-demographic and life-style factors associated with hypertension in study participants.
Blood pressure status
Hypertensive (N=114) Non-hypertensive (N=197) Crude odds ratio
n (%) n (%) [95% cu p-value
Age (years), median (Interquartile range) 49.0 [40.8-57.0] 42.0 [37.0-48.0]
Sex Male 1.07 [1.06-1.10] <0.00130 (26.3) 44 (22.3) 1.24 [0.73-2.12]
Female 0.42784 (73.7) 153 (77.7)
Educational level Tertiary/Professional 1.004 (3.5) 5 (2.5) 0.96 [0.23-4.06]
Secondary 0.95631 (27.2) 64 (32.5) 0.58 [0.28-1.21]
Basic/Primary 0.14659 (51.8) 104 (52.8) 0.68 [0.35-1.34]
None 0.26320 (17.5) 24 (12.2)
Religion 1.00Moslem 16 (14.0) 19 (9.6)
Christianity 1.53 [0.75-3.11] 0.24098 (86.0) 178 (90.4)
Marital status Single 1.0021 (18.4) 36 (18.3)
Widowed/Divorced/Separated 1.12 [0.59-2.13] 0.73947 (41.2) 73 (37.1)
Married/Co-habiting 1.23 [0.74-2.05] 0.42446 (40.4) 88 (44.7)
Employment status 1.00Unemployed 20 (17.5) 18 (9.4)
Employed 2.12 [1.07-4.19] 0.03294 (82.5) 179 (90.9)
Smoking status Ever smoker 1.005 (4.4) 8 (4.1)
Never-smoker 1.08 [0.35-3.40] 0.890109 (95.6) 189 (95.9)
Alcohol consumption 1.00Drinker 37 (32.5) 61 (31.0) 1.07 [0.65-1.76]
Abstainer 0.78577 (67.5) 136 (69.0)
Family history of CVD 1.00Present 21 (18.4) 18 (9.1) 2.25 [1.14-4.42]
Absent 0.01993 (81.6) 179 (90.9)
Fruit intake 1.00Rare/Never 79 (31.4) 20 (33.9)
Most at times 0.89 [0.49-1.62] 0.705173 (68.7) 39 (66.1)
Exercising RarelNever 1.0088 (77.2) 128 (65.0)
Most at times 1.82 [1.08-3.09] 0.02526 (22.8) 69 (35.0) 1.00
CI=Confidence interval; CVD=cardiovascular disease
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Table 17. Univariate analysis of anthropometric and metaboliclbiochemical factors associated with hypertension in study participants
Blood pressure status
Characteristic Hypertensive (N=114) Non-hypertensive (N=197) Crude odds ratio
n (%) n (%) [95% CI] p-value
Current body mass index 2:25.0 kg/nr' 67 (58.8) 88 (44.7) 1.77 [1.12-2.82] 0.017
<25.0 kg/nr' 47 (41.2) 109 (55.3)
Abdominal obesity (WHR) 1.00Present 44 (38.6) 71 (36.0) 1.12 [0.69-1.80] 0.653
Absent 70 (61.4) 126 (64.0)
Abdominal obesity (WC) 1.00Present 26 (22.8) 31 (15.7) 1.58 [0.88-2.83] 0.122
Absent 88 (77.2) 166 (84.3)
Fasting plasma glucose 1.00Elevated 13 (11.4) 18 (9.1) 1.28 [0.60-2.72] 0.521
Normal 101(88.6) 179(90.9)
Total Cholesterol 1.00Hypercholesterolemia 72 (63.2) 91 (46.2) 2.00 [1.24-3.20]
Normal total cholesterol 0.00442 (36.8) 106 (53.8)
HDL-cholesterol 1.00Abnormal 10 (8.8) 21 (10.7) 0.81 [0.37-1.78]
Normal 0.593104(91.2) 176(89.3)
LDL-cholesterol 1.00Elevated 51(44.7) 65(33.0) 1.64 [1.02-2.64] 0.040
Normal 63 (55.3) 132 (67.0)
Triglycerides 1.00Elevated 5(4.4) 6(3.1) 1.46 [0.44-4.90] 0.540
Normal 109 (95.6) 191 (96.9)
Estimated glomerular filtration rate Reduced 1.0026 (22.8) 22 (11.2) 2.35 [1.26-4.38] 0.007
Normal 88 (77.2) 175 (88.8) 1.00
CI=Confidence interval; HDL=High-density lipoprotein; LDL=Low-density lipoprotein; WC=Waist circumference; WHR=Waist-to-hip
ratio
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Table 18.Univariate analysis of HIV/ART-related factors associated with hypertension in study participants
Characteristic Blood pressure status
Hypertensive (N=114) Non-hypertensive (N=197) Crude odds ratio
n (%) n (%)
HIV sub-type HIV-II only [95% CI] p-value1 (0.9) 6 (3.1)
Mixed (Type I and II) 0.31 [0.04-2.61] 0.28033 (28.9) 43 (21.8)
HIV-Ionly 1.42 [0.84-2.41] 0.294
Nadir CD4+ T-cell count <350 cells/ul. 80 (70.2) 148 (75.1) 1.00
82 (71.9) 138 (70.1)
~350 cells/ul, 1.10 [0.66-1.82] 0.72632(28.1) 59(29.9)
Current CD4+ T-cell count <350 cells/ul. 1.0032 (28.1) 50 (25.4)
>350 cells/ul. 1.15 [0.68-1.93] 0.60482 (71.9) 147 (74.6)
ART exposure ART-exposed 1.00104 (91.2) 148 (75.1)
ART-naive 3.44 [1.67-7.11] 0.001
Duration of HIV infection (years), median (IQR) lO (8.8) ._4_9--'.(2_4_.9-'-) ~ 1.00 _
Duration of ART administration (years), 9.3 [5.7-11.4J 7.0 [4.1-10.2J 1.11 [1.04-1.18] 0.002
median (IQR)
7.0 [3.4-10.0]
Presence of co-morbidities 4.3 [0.0-8.1]Present l.14 [1.08-1.21] <0.00127 (23.7) 41 (20.8)
Absent 1.18 [0.68-2.05]87 (76.3) 0.555156 (79.2)
CI= Confidence interval; ART=Highly Active Anti-retroviral Therapy; IQR=Interquartile range 1.00
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4.3.2 Multiple logistic regression analysis of factors associated with
hypertension
Table 19 shows result of the multiple logistic regression of factors associated with
hypertension using a purposeful selection of variables method. Among the socio-
demographic and life-style factors studied, age, positive family history of
cardiovascular disease and exercising were significantly associated with hypertension
(Table 19). Current body mass index of~25.0 kg/rrr' and abdominal obesity due to high
waist circumference were also significantly associated with hypertension. Presence of
hypercholesterolemia was significantly associated with hypertension but not elevated
levels ofLDL-cholesterol (Table 19). Among the HIV/ART-related factors examined,
ART exposure and duration of ART administration were associated with hypertension.
Study participants who were ART-exposed had increased odds of hypertension
compared with those who were ART-naive (aOR=5.84 [95% CI, 2.23-15.31]; p<O.OOI)
and the odds of hypertension increases by 15% (95% CI, 1.09-1.22; p=OOI) for every
one-year increase in ART administration (Table 19).
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Table 19. Multiple logistic regression analysis of factors associated with hypertension
Adjusted odds ratio
Characteristic [95% CI] p-value
Age (years) 1.10 [1.06-1.14] <0.001
Family history ofhypertension/CVD
Present 2.23 [1.02-4.86] 0.045
Absent 1.00
Exercising
Rare/Never 1.94 [1.06-3.55] 0.032
Most at times 1.00
Current body mass index
2:25.0 kg/m2 2.18 [1.24-3.83] 0.007
<25.0 kg/rrr' 1.00
Abdominal obesity (waist circumference)
Present 2.15 [1.08-4.27] 0.029
Absent 1.00
Total cholesterol
Hypercholesterolemia 2.86 [1.30-6.28] 0.009
Normal total cholesterol 1.00
LDL-cholesterol
Elevated 0.69 [0.32-1.53] 0.365
Normal 1.00
ART exposure
ART -exposed 5.84 [2.23-15.31] <0.001
ART-naive 1.00
Nadir CD4+ T-cell count
::;350 cells/uL 0.51 [0.25-1.04] 0.064
>350 cells/uI. 1.00
Duration of ART administration (years) 1.15 [1.09-1.22] 0.001
Duration of HIV infection (years) 1.05 [0.97-1.13] 0.194
CI=Confidence interval; CVD=cardiovascular disease; ART= Anti-retroviral Therapy;
LDL=Low-density lipoprotein
Post-estimation analysis indicated that the generated logistic model was "good" on
"discrimination" with an area under the receiver operating characteristics curve of 0.81
(95% CI, 0.75-0.85; p<O.OOI) (Figure 5). In terms of "calibration", the generated model
gave a Hosmer-Lemeshow goodness-of-fit test y} value of 4.49 (p=0.81 0) indicating no
evidence of lack of goodness of fit between the predicted probabilities and the "true"
probabilities.
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o
1o.0
1.0
No
o
~
o
0.00 0.25 0.50 0.75 1.00
1 - Specificity
Area under ROC curve = 0.8130
Figure 5. Receiver operating characteristics for "calibration" of final multiple logistic
regression model of factors associated with hypertension
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4.4 Propensity score-matching analysis of ATT of ART on hypertension
4.4.1 Characteristics of ART -exposed and ART-naive study participants in
the PSM analysis
A total of 18 covariates (covariates either hypothesised to be associated with both ART
exposure and hypertension or hypothesised to be associated with hypertension;
excluding covariates which are affected by ART exposure/treatment) were examined
for inclusion in estimating the propensity scores (Table 20). Table 20 analysis indicates
that only baseline CD4 T-cell count was significantly different (at a=0.05) between the
ART-exposed group and the ART-naive group, suggesting its potential as a confounder
to the effect of ART treatment and thus needed to be adjusted.
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Table 20. Characteristics of study participants in propensity score-matching analysis
ART -exposed ART-naive
Characteristics N=252 N=59 p-value
Age (years), mean ± SD 45.6 ± 9.7 43.8 ± 8.7 0.147
Sex, n (%)
Female 193 (76.6) 44 (74.6)
Male 59 (23.4) 15 (25.4) 0.744
Education, n (%)
None 35 (13.9) 9 (15.2)
Basic 139 (55.2) 24 (40.7)
Secondary 70 (27.8) 25 (42.4)
Tertiary 8 (3.2) 1 (1.7) 0.129
Religion
Christianity 225 (89.3) 51 (86.4)
Moslem 27 (10.7) 8 (13.6) 0.534
Employment, n (%)
Unemployed 31(12.3) 7 (11.9)
Employed 221 (87.7) 52(88.1) 0.926
Marital status, n (%)
Single 47 (18.6) 10(16.9)
Married/Co-habiting 104 (41.3) 30 (50.9)
Divorced/Separated/Widowed 101 (40.1) 19 (32.2) 0.394
Smoking status, n (%)
Never smoker 242 (96.0) 56 (94.9)
Ever smoker 10 (4.0) 3 (5.1) 0.700
Alcohol status, n (%)
Abstainer 175 (69.4) 38 (64.4)
Drinker 77 (30.6) 21 (35.6) 0.453
Family history of CVD, n (%)
No 219(86.9) 53 (89.8)
Yes 33(13.1) 6 (10.2) 0.541
Exercise, n (%)
Never/Rare 175 (69.4) 41 (69.5)
Most times 77 (30.6) 18 (30.5) 0.994
Fruit intake, n (%)
Never/Rare 79 (31.4) 20 (33.9)
Most times 173 (68.6) 39 (66.1) 0.705
Baseline BMI (kg/m/), mean ± SD 25.7 ± 5.1 25.5 ± 5.2 0.836
BMI category, n (%)
<25.0 kg/rrr' 128 (50.8) 28 (47.5)
2:25.0 kg/m? 124 (49.2) 31 (52.5) 0.645
BMI=Body mass index; CVD=Cardiovascular disease; N=Total number of study
participants; n=Number of respondents per characteristic; SD=Standard deviation
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Table 20 cont'. Characteristics of study participants in propensity score-matching
analysis
ART -exposed ART-naive
Characteristics N=252 N=59 p-value
HIV-type
HIV-I 184 (73.0) 44 (74.6) 0.937
HIV-II 6 (2.4) 1 (1.7)
Mixed (HIV -I and HIV -II) 62 (24.6) 14 (23.7)
Duration of HIV infection (years),
mean± SD 7.8 ± 3.7 7.2 ± 3.7 0.271
Baseline CD4 T-cell count,
mean± SD 302.0± 147.7 384.2 ± 165.6 <0.001
Baseline CD4 T-cell count, n (%)
:::;350cells/ul. 150 (59.5) 26(44.1)
>350 cells/uL 102 (40.5) 33 (55.9) 0.031
Baseline sBP, mean ± SD (mmHg) 113.6± 17.6 116.1 ± 17.0 0.322
Baseline dBP, mean ± SD (mmHg) 69.3 ± 11.5 71.5 ± 11.8 0.199
Antihypertensive treatment, n (%)
No 209 (82.9) 49 (83.0)
Yes 43917.1) 10 (17.0) 0.983
Co-morbid condition, n (%)
No 197 (78.2) 46 (78.0)
Yes 55 (21.8) 13 (22.0) 0.972
BMI=Body mass index; dBP=diastolic blood pressure; N=Total number of study
participants; n=Number of respondents per characteristic; sBP=systolic blood pressure;
SD=Standard deviation
4.4.2 Estimation of propensity scores for ART -exposed and ART -naive
study participants in the PSM analysis
A logistic regression model was used to estimate the propensity score for each study
participant with the covariates; age, sex, education, marital status, employment,
smoking status, alcohol drinking status, family history ofCVD, fruit intake, exercising,
baseline BMI category, HIV type, baseline systolic blood pressure, baseline diastolic
blood pressure, anti-hypertensive medication and baseline CD4 T-cell count. The mean
± standard deviation of the estimated propensity scores was 0.81 ± 0.08 and the median
was 0.82 (IQR, 0.76-0.87). Although this may indicate skewness of the estimated
propensity scores, the skewness value of -0.80 is within the range of -1.0 to 1.0 and thus
considered good to demonstrate a normal distribution (Nduka et al., 20 16b). The results
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also indicate that the estimated propensity scores were grouped into five optimal blocks.
The "common support" for ART-exposed ranged from 0.5599 to 0.9616 with a mean
of 0.8186 and for ART-naive ranged from 0.5128 to 0.9182 with a mean of 0.7748. A
box-plot, a k-density plot and a histogram of the estimated propensity scores indicating
the region of "common support" are shown in figures 6, 7 and 8 respectively. Visual
analysis of the box plot (Figure 6) indicate a wide area (over 70%) of estimated
propensity scores overlap between the ART-exposed and the ART-naive study
participants as depicted by their median points. Figure 7 further collaborates this
observation as the shapes of the normal distribution of the estimated propensity scores
for ART-exposed and HAAT-naive were similar. Figure 8 confirms the visual
inspection of the distribution of the estimated propensity scores being normally
distributed and further indication of a high likelihood of achieving a region of common
support. In addition, test of balancing property of the estimated propensity scores for
each covariate was balanced for all the 18 covariates.
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I '
••
•
HAART-naive HAART -exposed
Figure 6. Box-plot of estimated propensity score among ART-exposed and ART-
naive study participants before matching
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N
o
.5 .6 .7 .8 .9 1
x
-- HAART~naive -- HAART~exposed I
Figure 7. K-density plot of estimated propensity scores among ART-exposed and
ART- naive study participants before matching
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HAART -naive HAART -exposed
.5 1 .5
Estimated propensity score
Graphs by HAART -exposure
Figure 8. Histogram of estimated propensity score among ART-exposed and ART-
naive study participants before matching
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4.4.3 Matching of ART-exposed to ART -naive study participants using the
estimated propensity scores in the PSM analysis
The kernel weighting strategy was used to match the ART-exposed group to the ART-
naive group with a common bandwidth of 0.06. There were 311 study participants (252
ART-exposed individuals and 59 ART-naive individuals) before matching. Matching
on the kernel weight yielded 295 study participants made up of 236 ART-exposed
individuals and 59 ART-naive individuals (final model). Table 21 shows the variance
ratios of the umnatched and matched data. The mean and the median standardised bias
reduced from 10.5% and 8.9% respectively in the umnatched data to 4.1% and 3.8% in
the matched data. The individual variance ratio in the matched data ranged from 0.92
to 1.22. The total variance ratio of the matched data was 1.72 indicating acceptability
of the matching procedure. Figure 9 shows the distribution of estimated propensity
scores before and after matching which indicates a good matching between the ART-
exposed and ART-naive study participants with a minimal loss of individuals due to
lack of "common support". Figure 10 shows the standardized percentage bias across all
covariates before and after matching. Visual inspection shows a reduction in bias after
matching as seen by a decrease in percentage bias spread from about -20 to 30 in the
umnatched data to about -8 to 8 in the matched data (Figure 10). Figure 11 shows the
standardized percentage bias for each covariate before and after matching also
indicating a successful matching of the ART-exposed to the ART-naive study
participants. Analysis of Figure 11 indicate a reduction in standardized percentage bias
for 14 included covariates after matching.
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Table 21. Covariates balance indicators in the propensity score-matching study
participants
ART- ART-
Covariate ex~osed naive p-value % bias Variance
Means ratio
Age (years) 44.95 45.37 0.613 -4.5 1.05
Sex 0.24 0.23 0.794 2.4
Religion 1.11 1.09 0.408 7.1 0.81
Education 2.22 2.28 0.342 -8.6 0.95
Marital status 1.94 1.96 0.786 -2.5 1.00
Employment status 1.12 1.11 0.706 3.4 1.09
Smoking 0.04 0.04 0.741 2.8
Alcohol 0.31 0.31 0.977 0.3
Family history of CVD 0.11 0.12 0.847 -1.8
Fruit intake 1.33 1.31 0.654 4.1 1.03
Exercising 1.71 1.68 0.483 6.4 0.95
Baseline BMI category 1.51 1.51 0.971 -0.3 1.00
HIV type 1.52 1.58 0.426 -7.5 0.94
Duration of HIV
infection (years) 7.54 7.80 0.544 -7.0 0.92
Baseline sBP (mmHg) 114.57 114.66 0.953 -6.1 1.03
Baseline dBP (mmHg) 70.03 70.75 0.503 -6.1 0.92
On anti-hypertensive
treatment 0.17 0.16 0.825 2.0
Baseline CD4 T-cell
count category 1.58 1.55 0.494 6.3 0.98
BMI=Body mass index; CVD=Cardiovascular disease; dBP=diastolic blood pressure;
sBP=systolic blood pressure
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.5 .6 .7 .8 .9
Propensity Score
HAART -naive HAART -exposed
Figure 9a. Distribution of estimated propensity score before matching
.5 .6 .7 .8 .9
Propensity Score
HAART-naive: On support HAART-exposed: On support
HAART-exposed: Off support
Figure 9b. Distribution of estimated propensity score after matching
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Unmatched
co
0
co
~
~
~
N
0
0
-32 -24 -16 -8 0 8 16 24 32
Standardized % bias across covariates
Matched
co
~
8
~
0
N
~
0
-32 -24 -16 -8 0 8 16 24 32
Standardized % bias across covariates
Figure 10. Histogram showing standardized percentage bias before and after
propensity score matching
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BLCD4Cat ~ ..
Age )( .
MaritalStatus ~ .....................................•........................
HIVDurYrs · · ·x ..· ·..· .
FamCVD ~ .
HIVType ···x······ .. ·· .. · ..
EmploymentCat ........................................•. ·x· .. ·· , ..
AntiHPT ·t·)(························································
BLExercise ~ )( .
Gender )(- .
Smoking ···x·· ..···..······..· ·..····· ·········..·..·..·
BLFruit )(- .
BLBMICat ) ..
Religion )( .
Alcohol·" ~ .
Education .............•....... x·············· .
BLsBP )( .................................• Unmatched
BLdBP ·x.. x Matched
I I I I I
-20 -10 0 10 20 30
Standardized % bias across covariates
Figure 11. Standardised % bias across each covariate before and after matching
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4.4.4 Estimation of ATT of ART exposure on hypertension and blood
pressure values in the PSM analysis
The prevalence of hypertension in the ART-exposed and ART-naive study participants
in the final propensity score-matching sample was 42.4% (95% CI, 36.2-48.8) and
17.0% (95% CI, 9.3-28.9) respectively and the estimated ATT was 26.2% (p<O.OOI)
indicating a statistically significant difference between the ART -exposed group and the
ART-naive group (Table 22). Thus, ART-exposed patients were 26.2 percent points
more likely to be hypertensive compared with ART-naive patients. Post estimation
analysis indicated a gamma level of 1.6 at 95% confidence interval meaning the
estimated average treatment effect of ART exposure on hypertension is sensitive to an
unobservable covariate, which will increase the odds of ART exposure by 60% (Table
22). In addition, at gamma (f) level of 2.l, the Hodges- Lehmann point estimates
encompass zero indicating that a high increase in the likelihood of being an ART-
exposed individual due to an unobservable characteristic which also increases being
hypertensive is required to explain the observed level of estimated average treatment
effect (Table 22).
The estimated ATT on systolic blood pressure was 12.0 mmHg (95% CI, 5.7-18.3;
p<O.OOI) indicating a significant difference in systolic blood pressure between the
ART-exposed group and the ART-naive group (Table 18). Similarly, the estimated
ATT on diastolic blood pressure was 6.1 mmHg (95% CI, 1.3-10.8; p=0.012) indicating
a statistically significant difference in diastolic blood pressure between the ART-
exposed group and the ART-naive group (Table 22). Post estimation sensitivity
analyses indicate a gamma level of2.5 and 1.9 at 95% confidence interval respectively
for systolic blood pressure and diastolic blood pressure (Table 22). Gamma levels for
Hodges-Lehmann point estimates were 3.4 for systolic blood pressure and 2.4 for
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diastolic blood pressure (Table 22), indicating results of the estimated treatment effect
of ART exposure on systolic and diastolic blood pressures were insensitive to selection
on unobservable covariates i.e. unlikely that such confounding variables would not
have been observed.
Table 22. Estimated average treatment effect of ART exposure on hypertension and
blood pressure values
Estimated Gamma {r}
Outcome ATT [95% CI] p-value 9S%CI Hodges- Lehmann
HYf~ertension, % 26.2 [13.3-39.1] <0.001 1.6 2.1
Systolic blood
Eressure (mmHg) 12.0 [5.7-18.3] <0.001 2.5 3.4
Diastolic blood
pressure (mmHg) 6.1 [1.3-10.8] 0.012 1.9 2.4
ATT=Estimated Average treatment effect on the treated; CI=Confidence interval
4.5 Cardiovascular disease risk score assessment
Table 23 shows the various CVD risk score assessment classified as low, medium or
high. Majority of the study participants were classified as low risk of cardiovascular
disease in the 10-year FRS (79.4%) and 10-year WHO/ISH prediction chart (86.8%).
With the 5-year D:A:D risk score, over 50% of the study participants were classified as
moderate to high risk ofCVD (Table 23).
Table 23. Cardiovascular risk assessment score
Cardiovascular risk All participants _:"'C_o_m_p=-a._s..or._~in_w..;;i.;.;;th.;;..,;;;,D..;.:A;.;;;.;.;:D;;;;...;;s..;;c..;.o.;;..re;..._
scoring system n, %' Kappa Kappa Kappa
~M--od-if-ie-d--F-R-S------Low 247(79.4)
----------------- Moderate 47 (15.1) 0.34 <0.001 64.0%
High 17 (5.5)
WHO/ISH risk Low 270 (86.8)
prediction score Moderate 19(6.1)
0.10 <0.001 50.2%
High 22 (7.1)
Low 148 (47.6)
D:A:D risk score Moderate 148 (47.6)
High 15 (4.8)
'Column percentages within each super row; D:A:D=Data Collection on Adverse
Events of Anti-HIV Drugs; FRS=Framingham risk score; ISH=Intemational Society
for Hypertension; WHO=World Health Organisation
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4.6 Summary of results
The key findings of this study involving 311 study participants of which 76.5% were
females and 73.0% were ~40 years were as follows;
1. The prevalence of hypertension in the study participants was 36.7% [95%
Cl, 31.3-42.3]. The prevalence of hypertension in study participants aged
~40 years was 40.5% [95% Cl, 16.8-35.6] which was significantly higher
than the prevalence in the study participants aged <40 years (26.2% [95%
Cl, 16.8-35.6]). Similarly, the prevalence of hypertension in ART-exposed
group (41.3% [95% CI, 35.2-47.3]) was significantly higher than the
prevalence in the ART-naive group (16.9% [95% CI, 7.4-26.5]).
2. Propensity score-matching analysis indicated that the estimated average
treatment effect (on the treated-ATT) of ART on hypertension was
significant at 26.2 percent points. Similarly, the ATT of ART on systolic
blood pressure and diastolic blood pressure values were significant at 12.0
mmHg and 6.1 mmHg respectively.
3. Regression modelling showed that the socio-demographic and life-style
factors associated with hypertension were increasing age (aOR = 1.10 [95%
CI, 1.06-1.14]), positive family history of CVD/hypertension (aOR=2.23
[95% CI, 1.02-4.86]) and minimal exercising (aOR=I.94 [95% cr, 1.06-
3.55]). Anthropometric and Metabolic/biochemical factors associated with
hypertension were current body mass index of ~25.0 kg/rrr' (aOR=2.l8
[95% CI, 1.24-3.83]), abdominal obesity (high waist circumference)
(aOR=2.15 [95% CI, 1.08-4.27]) and total cholesterol level ~5.17 mmoliL
(aOR=2.86 [95% CI, 1.30-6.28]). HIV/ART-related factors associated with
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hypertension were exposure to ART (aOR=5.84 [95% CI, 2.23-15.31]) and
increasing duration of ART exposure (aOR=1.15 [95% CI, 1.09-1.22]).
4. Risk of CVD was estimated using three different CVD risk-scoring
systems. Whilst 20.6% and 13.1% of the study participants were of
moderate to high risk of CVD using the 10-year general FRS and the 10-
year WHO/ISH risk prediction chart respectively, as many as 52.4% were
of moderate to high risk of CVD using the 5-year D:A:D CVD risk score.
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CHAPTER FIVE
5.0 DISCUSSION
5.1 Introduction
The present study was undertaken to determine the prevalence of hypertension and
associated factors among patients attending HIV clinic at the KBTH in Accra. Results
indicate that the prevalence of hypertension was 36.7% and the factors associated with
hypertension were increasing age, positive family history of CVD/hypertension,
minimal exercising, current BMI ~25.0 kg/m", total cholesterol level ~5.17 mmol/L,
exposure to ART and increasing duration of ART exposure. Findings from the
propensity score-matching analysis indicate that treatment with ART results in an
estimated average increase in both systolic blood pressure and diastolic blood pressure
values (12.0 mmHg and 6.1 mmHg respectively) and a further increase in the risk of
hypertension of 26.2%. Estimation of cardiovascular disease risk score indicate 52.4%
of the study participants were of moderate to high risk ofCVD using the 5-year D:A:D
CVD risk score.
5.2 General characteristics of study participants
Results of the present study indicate a study population with majority (73%) of them
aged ~40years. This is similar to the age structure reported in most cross-sectional or
cohort studies in HIV-infected persons in SSA (Denue et al., 2012; Diouf et aI., 2012;
Schwartz et aI., 2012; Kagaruki et al., 2014; Mashinya et al., 2014; Nsagha et aI., 2015).
Current report from the Ghana National AIDS Control Programme indicate the age
group 45-49 years constitute the highest prevalence (5.7%) of HIV -positive patients in
Ghana (NACP, 2017). This clearly shows an ageing population ofPLHIV in Ghana and
SSA in general and therefore the need to focus additionally on age-related morbidities
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including cardiovascular events and its associated factors in order to sustain the gains
made in the roll out of ART programme in HIV care.
More than three-quarters of the study participants in this study were females (76.2%)
which is comparable to other studies in SSA (Awotedu et aI., 2012; Ekali et aI., 2013;
Bloomfield et aI., 2014b; Dimodi et aI., 2014; Sander et aI., 2015; Dimala et aI., 2016).
This high proportion of females in studies in PLHIV in SSA could be attributed to
several factors including the health seeking behaviours of SSA populations and the
policy of screening (HIV testing) all consenting pregnant women in order to manage
all HIV -infected pregnant women to prevent mother-to-child HIV transmission. In
Ghana, prevalence and incidence rates of HIV infection are obtained from sentinel sites,
which are mostly antenatal service centres. Additionally, the main route of HIV
transmission in SSA is heterosexual unlike most European and North America
countries, making women more vulnerable to HIV infection than men.
In this cross sectional study, 81% of the study participants were on ART. Although this
figure is high, it is comparable to similar observational studies conducted in SSA (Ekali
et aI., 2013; Dimodi et aI., 2014; Nsagha et aI., 2015; Nduka et al., 2016b). A cross
sectional study of265 PLHIV in Nigeria reported of an ART-exposed group of 80.8%
(Edward et aI., 2013). Contributing factors to this high proportion of ART-exposed
group could be the continuous policy modification of the CD4+ T cell count threshold
for initiation of ART in Ghana (based on WHO recommendations) which has shifted
from an initial <200 cell/ul. to a current threshold of :S500 cells/ul. thereby
incorporating more HIV-positive individuals to start ART (WHO, 2013c; NACP et aI.,
2014).
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5.3 Prevalence of hypertension in patients attending HIV clinic at KBTH
The present study revealed that the prevalence of hypertension in PLHIV attending HIV
clinic at KBTH in Accra was 36.7%. Hypertension is the leading risk factor for CVD
and cerebrovascular mortality (WHO, 2009b) and has been reported to be common
among PLHIV (Barbaro, 2006). Several prevalence figures of hypertension in PLHIV
have been reported globally ranging from 19.9% in Brazil (Silva et aI., 2009; Arruda
Junior et aI., 2010) to 31.5% in Southern America Cohort (Cahn et aI., 2010). Studies
from most developed countries suggest prevalence of hypertension among PLHIV
between 8% and 39% (Jerico et aI., 2005; Barbaro, 2006). Among patients on ART,
studies in Europe and North America report of hypertension prevalence between 20%
and 40 % (Bergersen et aI., 2003; Gazzaruso et aI., 2003; Palacios et aI., 2006; Baekken
et al., 2008; Coloma Conde et al., 2008; Mothe et al., 2009). Reports have cited a higher
burden of hypertension (in the general population) in sub-Saharan Africa compared
with other sub-regions (Addo et al., 2007). An estimate of the burden ofNCDs in SSA
put the prevalence of hypertension in the general population at 16.2% (Twagirumukiza
et al., 2011; Ogah and Rayner, 2013). Results of other studies in sub-Saharan Africa
among PLHIV (ART-exposed and ART-naive combined) indicate prevalence of
hypertension ranging from a low of 14.0% in Botswana to a high of 32.0% in Kenya
(Nyabera et aI., 2011). The prevalence of hypertension in this study is therefore
comparable to other studies in PLHIV in SSA.
In relation to Ghana, systematic reviews have cited prevalence of hypertension in the
general population to be between 19.3% and 54.6% (Bosu, 2010; Addo et al., 2012)
and a 20] 1 al1lmal hospital-based report from the Ministry of Health indicate a
prevalence of hypertension between 30% and 40% (MOH, 2011). The present
hypertension prevalence can therefore be said to be a reflection of the current high
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prevalence of hypertension in Ghana, especially as the study was conducted ill a
hospital setting and in patients with a chronic infectious disease.
The present study also indicated a hypertension prevalence of 40.5% in study
participants' aged 2:40 years which is comparable to a combined analysis of data from
MACS and Women's Interagency HIV Study (WIHS) report of a 35%-41 % prevalence
of hypertension in patients 2:40 years of age (Kaplan et aI., 2007).
In the present study, the 41.3% prevalence of hypertension in ART-exposed study
participants was significantly higher compared with the ART-naive group. The current
study prevalence of hypertension in ART-exposed PLHIV is comparable with other
studies reported in sub-Saharan Africa of 65% in Botswana (Shapiro et aI., 2012),
38.0% in Cameroon (Dimala et aI., 2016) and 34.9% in Zimbabwe (Mutede et aI.,
2015). A hypertension prevalence of 45% in ART-exposed individuals in Malaysia has
been reported (Hejazi et aI., 2013 ). Whilst some studies, both in Europe and SSA have
indicated no significant difference in hypertension prevalence between ART-exposed
and ART-naive patients (Bergersen et aI., 2003; Edward et aI., 2013; Dimodi et aI.,
2014; Ogunmola et aI., 2014; Sander et aI., 2015), other studies have indicated
otherwise (Gazzaruso et aI., 2003; Muhammad et aI., 2013; Kagaruki et aI., 2014; Peck
et aI., 2014; Dimala et aI., 2016). It is important to note that a previous study conducted
in Ghana determining the prevalence of hypertension in ART-exposed PLHIV
indicated a systolic hypertension prevalence of 15.2% and a diastolic hypertension
prevalence of23.8% (Ngala and Fianko, 2014). Whilst the results of this study is higher
than the study reported by Ngala and Fianko (2014), this could be attributed to the fact
that present study indicated a mean duration of ART exposure of 84 months and was
conducted in the largest cohort of HIV -care in Ghana. Duration of ART exposure has
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been shown to be associated with hypertension in several studies (Bergersen et al.,
2003; Seaberg et al., 2005; Ekali et al., 2013; Ngala and Fianko, 2014; Mutede et aI.,
2015; Nduka et al., 2016a) and in addition, the mean age of the study participants for
the study reported by Ngala and Fianko (2014) was 38 years whilst that for the current
study was 45 years. Age is a known risk factor for several cardiovascular diseases and
hypertension (especially age 2:40 years). Other factors, which may account for this
differential observation, could be differences in geographical locations, study settings,
clinical characteristics of study participants and even study design.
Bearing in mind the continuous implication of ARV s and in particular PIs in the
pathophysiology of hypertension in PLHIV, it is imperative that with an adequate study
sample sizes, there will be significant difference in prevalence of hypertension between
ART-exposed groups and ART-naive groups. Both HIV infection and ARVs have been
postulated to have direct and indirect effect on the vascular system accelerating
atherosclerosis via endothelial dysfunction, vasculitis, HIV nephropathy, lipodystrophy
and other metabolic derangements (Bergersen et al., 2003; Hsue et al., 2012; Boccara
et al., 2013; Nuesch et al., 2013; Bloomfield et aI., 2014b; Chastain et al., 2015; Isa,
2015).
It is worth noting that the observed prevalence of hypertension in this study could be
slightly lower than it actually is, given that potential study participants who were
already diagnosed with hypertension before emolment into the HIV clinic were
excluded from the study.
5.4 Factors associated with hypertension
Using regression modelling and purposeful selection of covariates method, results from
the present study indicate that the factors associated with hypertension were increasing
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age, family history of cardiovascular disease/hypertension, exercising, body mass index
and abdominal obesity. Other factors were total cholesterol, exposure to ART and
duration of exposure to ART.
Socia-demographic factors
Among the socio-demographic factors, increasing age is a well-known risk factor for
hypertension in the general population and this is particularly evidenced in people of
40 years and above. In the present study, the odds of hypertension increases by 10 %
for every additional one-year increase in age. This results is comparable and consistent
with several studies conducted in PLHIV in both developed and developing countries
(Friis-Moller et al., 2003; Seaberg et al., 2005; Bloomfield et aI., 2011; Medina-Tome
et al., 2012; Manner et al., 2013b; De Socio et al., 2014; Sander et al., 2015; Dimala et
aI., 2016; Njelekela et al., 2016). Hejazi et al. (2013) had reported ofa 7% increase in
odds of hypertension for everyone-year increase in age, which is comparable with the
results of the present study. The contribution of ageing to the pathogenesis of
hypertension has been attributed to arterial stiffness that is reduced elasticity of the
large arteries which occurs with ageing (Oparil et al., 2003). This reduced elasticity is
attributed to smooth-muscle hypertrophy and thinning, collagen deposition and
fragmenting and fracture of the elastin fibres in the arteries (O'Rourke et al., 2000).
Ageing has also been associated with endothelial dysfunction which results in isolated
systolic hypertension (Safar, 1999). Some studies have argued about an accelerated
ageing process in PLHIV but this continues to be a subject of debate and research in
the light of the near-normal life expectancy ofPLHIV in the present ART era.
In the present study, other socio-demographic characteristics measured including sex,
educational level, religious denomination, marital status and employment status were
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not associated with hypertension. The lack of association between sex and hypertension
is consistent with reports from other studies (Jerico et aI., 2005; Palacios et aI., 2006;
Diouf et aI., 2012; Medina-Tome et aI., 2012; Edward et aI., 2013; Ikeda et aI., 2013;
Ogunmola et aI., 2014; Kwarisiima et aI., 2016). In a similar way, the lack of association
between highest educational status attained and hypertension is similar to reports from
other studies (Ikeda et aI., 2013; Ogunmola et aI., 2014; Kwarisiima et aI., 2016). Both
Krauskoff et al. (2013) and Hejazi et al. (2013) reported lack of association between
employment status and hypertension as also reported in the present study.
Cardiovascular diseases and risk factors have been known to run in families and
individuals with parental history of hypertension are classified as high risk targets for
interventions to prevent the disease (Whelton et aI., 2002; Wang et aI., 2008). In the
present study, a positive family history of CVD/hypertension was associated with
hypertension. This finding is consistent with other studies that have associated positive
family history of hypertension with increased odds of hypertension in PLHIV (Arruda
Junior et aI., 2010; De Socio et aI., 2014). Although some studies have reported no such
association (Thiebaut et aI., 2005; Dimala et aI., 2016), the fact that there are several
reports on the interaction between genetics, renal dysfunction and the resultant salt and
water retention imbalance which leads to hypertension in the general population (Oparil
et aI., 2003) supports the results of this study.
Life-style factors
Exercising, a life-style factor was established to be associated with hypertension in this
study. Several epidemiological studies have demonstrated the relevance of regular
physical exercise in reducing the incidence of hypertension in the general population
(Carnethon et aI., 2010; Warburton et aI., 2010; Diaz and Shimbo, 2013;
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Thawornchaisit et al., 2013). The results of the present study is therefore consistent
with the knowledge that physical inactivity is a risk factor for hypertension and is
comparable with the results of other studies conducted in PLHIV in SSA (Mute de et
al., 2015). Regular physical exercise is proposed to reduce the incidence of
hypertension through several mechanisms including decrease in oxidative stress (and
reactive oxygen species), decrease in inflammation and body weight and increase in
endothelial function (Diaz and Shimbo, 2013).
Smoking of tobacco is estimated to cause 10% of CVD and is considered a major risk
factor for hypertension in the general population. Results of the present study indicated
lack of association between smoking and hypertension which is similar to reports from
other countries in SSA (Ikeda et aI., 2013; Kagaruki et aI., 2014; Dimala et al., 2016).
This phenomenon could be attributed to the low prevalence of smoking generally in
SSA compared with most developed countries and the counselling given to HIV-
patients to stop smoking during HIV clinic emolment. The prevalence of smoking in
this study (both current and previous) was 4.2%.
Although alcohol drinking is considered a major risk factor in the incidence of
hypertension in the general population, the current study did not indicate such an
association. Results from other studies reported such a similar relationship (Arruda
Junior et aI., 2010; Kagaruki et aI., 2014; Mashinya et aI., 2015; Dimala et aI., 2016;
Kwarisiima et aI., 2016).
Anthropometric factors
Of the anthropometric parameters investigated in this study, BMI 2:25.0 kg/rrr', and
abnormal waist circumference (as a measure of abdominal obesity) were established to
be associated with hypertension. Several epidemiological studies have established
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overweight/obesity as a risk factor for the incidence of hypertension in the general
population (Brown et al., 2000; Sakurai et al., 2006), and among PLHIV (Friis-Moller
et al., 2003; Seaberg et al., 2005; Thiebaut et al., 2005; Medina-Tome et al., 2012; De
Socio et aI., 2014). Several studies among PLHIV in SSA have also reported of
increased odds of hypertension in overweight/obese individuals (Kagaruki et aI., 2014;
Mutede et al., 2015; Dimala et aI., 2016; Kwarisiima et aI., 2016; Njelekela et aI., 2016).
Results of the present study which indicated an increased odds of hypertension of 2.18
(95% CI, 1.24-3.83) in overweight/obese individuals is comparable to a report by
Bloomfield et aI. (2012), which studied a cohort of 12,194 HIV -positive study
participants in Kenya and indicated an increased odds of hypertension of2.42 (95% CI,
1.88-3.09) in individuals with BMI ~25.0 kg/rrr'. Many studies have drawn attention
to increasing prevalence of overweight/obesity in HIV -positive individuals and this has
been attributed to both a general age-related increase in body weight (Denue et aI.,
2012) and a tendency of PLHIV to be overweight to remove or reduce suspicion and its
accompanying stigmatisation of HIV infection (De Socio et al., 2008; Boodram et aI.,
2009; Aboud et aI., 2010; Muronya et aI., 2011; Edward et aI., 2013). In addition,
suggestions have been given that the trend for elevated blood pressure in ART-exposed
individuals may be due to both general obesity and distorted lipid profile mediation in
the association between ART and hypertension (Palacios et aI., 2006; Crane et aI., 2009;
Bernardino de la Serna et aI., 2010; Ekali et al., 2013).
Abnormal waist circumference, a measure of abdominal obesity or body fat
distribution, was found to be associated with hypertension in this study. Absolute waist
circumference> 102 em (in men) and >88 em (in women) is classified as abdominal
obesity (WHO, 2000; Yusuf et al., 2004). A large body of scientific evidence abounds
of the role played by abdominal obesity in the aetiology of cardio-metabolic
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abnormalities including hypertension, dyslipidaemia, insulin resistance and type 2
diabetes (Van Pelt et aI., 2001; Anjana et aI., 2004; Janssen et al., 2004; Yusuf et aI.,
2004; Cameron and Zimmet, 2008; Westphal, 2008). A mediation analysis for lineal
model conducted in a study by Nduka et aI. (2016c) emphasised the strong impact of
central fat distribution in mediating the causal pathway between ART and increased
blood pressure (Nduka et aI., 20 16c). This observation is supported by previous studies
that have indicated the mediation role played by waist circumference in the association
between ART and hypertension (Shlay et aI., 2007; Antonello et aI., 2015). The present
study is thus consistent with the study by Nduka et aI. (2016c) (as both ART and
abdominal obesity were associated with hypertension) and other studies (Arruda Junior
et al., 2010; Kagaruki et al., 2014).
Waist-to-hip ratio (WHR), another component of the anthropometric parameters was
not associated with hypertension, similar to results reported by other studies (Hejazi et
aI., 2013 ; Mashinya et al., 2014; Mashinya et aI., 2015; Dimala et aI., 2016).
M etabolic/biochemical factors
Reports from the present study indicate an association between hypercholesterolemia
and hypertension. Several studies have been conducted in the field of lipid profile of
HIV -infected individuals. Disorder of the lipoprotein metabolism referred to as
dyslipidaemia is regarded as a major risk factor for CVD. The most common form of
dyslipidaemia is hyperlipidaemia, which is characterised by hypercholesterolemia,
elevated concentration of LDL-C, hypertriglyceridemia and reduced level of HDL-C.
Results from the present study is thus consistent with several reports which associate
hypercholesterolemia with an increased risk of hypertension in the general population
(Sesso et al., 2005; Halperin et al., 2006) and among PLHIV (Bergersen et al., 2003;
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Thiebaut et al., 2005; Baekken et al., 2008; Diouf et al., 2012; Kagaruki et al., 2014).
The pattern of distorted lipid profile among PLHIV is seen mostly in patients on
protease inhibitors and PI-based regimens have been shown to be atherogenic more
than non PI-based regimens (van der Valk et al., 2001; Grinspoon and Carr, 2005;
Buchacz et al., 2008; Dau and Holodniy, 2008; Salami et al., 2009). Lipid abnormalities
due to PI-based regimens has been estimated to occur in up to 50% of HIV -infected
patients (Fontas et al., 2004) but the degree of this abnormality varies with the type of
protease inhibitor involved and the duration of treatment (Carr, 2003; Chastain et al.,
2015). However, in this study, the number of individuals on PI-based regimen were few
(10.6%, n=35) which may account for the lack of association between the other
parameters of the lipid profile measured (i.e. HDL-C, LDL-C and triglycerides) and
hypertension. Nonetheless, some studies among PLHIV have reported lack of
association between hypertriglyceridemia and hypertension (Thiebaut et al., 2005;
Dioufet al., 2012; Medina-Tome et al., 2012; Hejazi et al., 2013), lack of association
between HDL-C and hypertension (Bergersen et al., 2003; Palacios et a!., 2006; Arruda
Junior et al., 2010; Denue et al., 2012; Kagaruki et al., 2014) and lack of association
between LDL-C and hypertension (Palacios et al., 2006; Arruda Junior et al., 2010;
Denue et al., 2012).
Elevated fasting blood glucose (FPG) and reduced estimated glomerular filtration rate
(eGFR) are both recognised indices whose prevalence are high in hypertensive patients
in the general population. The prevalence of elevated FPG and reduced eGFR in the
present study were 10.0% and 15.4% respectively and these are comparable to other
studies in SSA. Studies by Edward et al. (2013) in Nigeria and Diouf et al. (2012) in
Senegal had reported a prevalence of elevated FPG of 10.6% and 14.5% respectively.
The frequency of reduced eGFR was 15.4%, which is higher than figures from previous
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studies of 12.6% in ART-naive and 10.7% in ART-exposed patients (Peck et al., 2014)
but comparable to a prevalence of 17.0% reported by Baekken et al. (2008). Previous
studies have noted that kidney disease is common in HIV -infected adults in SSA
(Msango et al., 2011). Literature abounds on the effect of ARVs on the incidence of
elevated FPG in association with hypertension and CVD among PLHIV (Chastain et
al., 2015). Results from the present study indicate lack of association between both
elevated FPG and reduced eGFR and hypertension. This is similar to reports from other
studies which indicated lack of association between elevated FPG and hypertension
(Bergersen et al., 2003; Baekken et aI., 2008; Hejazi et al., 2013) and between reduced
eGFR and hypertension (Baekken et al., 2008; Wilson et al., 2009; Denue et al., 2012;
Manner et al., 20l3b; Peck et al., 2014).
HIVIART-related factors
Several studies have been conducted in determining the relationship between HIV IART
and hypertension. In studies that have included HIV -negative individuals as controls,
the association between HIV status and hypertension has been conflicting. Some studies
have reported either a significant difference in hypertension prevalence between HIV-
negative controls and HIV-positive group or an association between HIV status and
hypertension (Malaza et al., 2012; Peck et al., 2014; Kwarisiima et al., 2016). Most
studies on the other hand have reported no association between HIV status and
hypertension (Bergersen et al., 2003; Jerico et al., 2005; Baekken et al., 2008; Arruda
Junior et al., 2010; Schwartz et al., 2012; Shapiro et al., 2012; Mashinya et al., 2014;
Ogunmola et al., 2014; van Zoest et al., 2016). Some reasons, which could account for
this disparity, include the differences in population structure between the studies, the
prevalence of hypertension in the general population and the duration of infection.
However, whilst some studies have associated longer duration of HIV infection with
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hypertension (Medina-Tome et al., 2012; Manner et al., 2013b; De Socio et al., 2014),
others have reported lack of such an association (Baekken et aI., 2008; Hejazi et aI.,
2013 ). Despite these conflicting results, studies that have associated HIV positive
status with hypertension have suggested mechanisms to account for the higher
incidence of hypertension in HIV positive individuals. These include the infection
inducing a dysregulated and chronic inflammation due to immune suppression and
reconstitution, which can lead to endovascular changes (Kaplan et al., 2008; Palella and
Phair, 2011; Peck et al., 2014). Other mechanisms are endothelial activation and
dysfunction as well as direct infection of the virus on arterial vascular smooth cells
(Kline and Sutliff, 2008; Torriani et al., 2008; Mulazzi et al., 2009) and the generation
of free radicals leading to oxidative damage to the endothelium and endothelial
dysfunction (Torre, 2006). The present study however did not include HIV -negative
controls in the design; hence, the relationship between HIV status and prevalent
hypertension was not investigated.
Other findings of the present study are the association between exposure to ART and
hypertension and the association between duration of ART and hypertension. Although
literature abounds in studies on the association between ART exposure and the risk of
hypertension, the results have been inconclusive. A systematic review with meta-
analysis of 39 studies involving 44,903 participants however concluded that both
systolic and diastolic blood pressure values were significantly higher among ART-
exposed patients compared with treatment-naive patients (Nduka et al., 2016a). The
study also reported of a significant increased risk of hypertension in ART-exposed
patients compared with treatment-naive patients. The findings of this study is therefore
consistent with several other studies in SSA (Shapiro et al., 2012; Muhammad et al.,
2013; Kagaruki et aI., 2014; Ngala and Fianko, 2014; Nsagha et al., 2015; Dimala et
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al., 2016; Nduka et al., 2016b) and other sub-regions (Bergersen et al., 2003; Chowet
al., 2003; Seaberg et al., 2005; Crane et al., 2006; Palacios et al., 2006; Baekken et al.,
2008; Wilson et al., 2009; De Socio et al., 2014). Whilst it may be overwhelming to
notice the amount of literature, including one systematic review and one propensity
score-matching analysis, supporting the association between ART and hypertension,
other studies have reported the lack of association between ART and hypertension in
SSA (Bloomfield et al., 2011; Dimodi et al., 2014; Ogunmola et al., 2014; Edwards et
al., 2015; Sander et al., 2015) and in other sub-regions (Bergersen et al., 2003; Friis-
Moller et al., 2003; Jerico et al., 2005; Thiebaut et al., 2005; Arruda Junior et al., 2010;
Medina-Tome et al., 2012; Ikeda et al., 2013; Krauskopfet al., 2013; Hejazi et al., 2013
; Antonello et al., 2015). The present study reported of an adjusted odds ratio of 5.84
(95% CI, 2.23-15.31) which is highly comparable with a similar study conducted in
Ghana which reported of an increased odds of hypertension of 5.00 in ART-exposed
individuals compared with ART-naive individuals (Ngala and Fianko, 2014). The
present finding is also supported by the plausible causal effect inferred from the PSM
analysis presented earlier in this thesis.
Various mechanisms have been postulated to account for the association between ART
and hypertension, These mechanisms include premature and/or accelerated
development of atherosclerosis leading to blockage of the blood vessels lumen (Carr,
2003; Fichtenbaum, 2003; Mehta and Reilly, 2005), ART induced immune activation,
increased intestinal bacterial translocation and low-grade inflammation which, may
promote atherosclerosis and increased arterial stiffness (Hsue et al., 2009; Crowe et aI.,
2010; Kaplan et al., 2011; Redd et al., 2011; Boccara et aI., 2013) and the involvement
of ARVs in both lipid and glucose metabolism resulting in lipodystrophy syndrome and
the activation of the renin angiotensin system (Boccara et al., 2010). Protease inhibitors
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in particular have been reported to induce endothelial injury, which is associated with
dyslipidaemia, oxidative stress and senescence (Lefevre et al., 2010). However, studies
by the AIDS Clinical Trials Group S1S2 which showed that ART rather improved
endothelial dysfunction in HIV patients after 24 weeks of therapy (Torriani et al., 2008)
has led to the probable differential effect of PIs on the risk of CVDs in general
depending on the length of usage.
Another finding in this study is the association between length of antiretroviral usage
and the increased risk of hypertension. This results is consistent with other reports of
an increased risk of hypertension with increasing duration of ART among PLHIV in
SSA (Ekali et al., 2013; Muhammad et al., 2013; Ngala and Fianko, 2014; Mutede et
al., 2015) and the results of the systematic review and meta-analysis (Nduka et al.,
2016a).
Meanwhile there is there is lack of association between CD4+ T-cell count, HIV
duration and hypertension in this study. The relationship between CD4+ T-cell count
as a measure of immune system function and hypertension in HIV infected individuals
continue to be a subject of ongoing research (Wilson et al., 2009). Large cohort studies
have associated lower CD4+ T cell count with incident cardiovascular event in general
and posited it could be mediated by a chronic pro-inflammatory state that promotes
atherosclerosis (El-Sadr et al., 2006; Friis-Moller et al., 2007; Lichtenstein et al., 2010).
On the other hand, a higher CD4+ T cell count, seen as a measure of better immune
system function has been associated with hypertension among PLHIV and this
phenomenon has been attributed to an overall improved general health and nutritional
status (with initiation of ART) such that the association is mediated by confounders like
obesity (Bergersen et al., 2003; Crum-Cianflone et al., 2010). Despite the mechanisms
suggested to be involved in the association of low CD4+ T-cell count with
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hypertension, several observational studies indicate lack of association between low
CD4+ T-cell count and hypertension. The present results is therefore consistent with
several other reports which have indicated lack of association between CD4+ T cell
count and hypertension among PLHIV in SSA (Bloomfield et aI., 2011; Denue et al.,
2012; Diouf et aI., 2012; Ogunmola et al., 2014; Okello et al., 2015; Dimala et al., 2016)
and other sub-regions (Jerico et aI., 2005; Palacios et aI., 2006; Medina-Tome et aI.,
2012; Ikeda et al., 2013; Benjamin et aI., 2016; van Zoest et aI., 2016).
Another finding in this study is the lack of association between duration of HIV
infection and prevalence of hypertension. Even though a cohort study would have been
appropriate to investigate a relationship between duration of HIV diagnosis and
preferable incident hypertension, nonetheless, the results of the present study is
consistent with reports from several previous studies (Jerico et aI., 2005; Palacios et al.,
2006; Bhaskaran et al., 2008; Denue et aI., 2012; Ikeda et al., 2013; Hejazi et al., 2013
; Dimala et aI., 2016).
5.5 Estimated average treatment effect of ART exposure on hypertension
and blood pressure values
The study demonstrated that using PSM analysis, the estimated average treatment effect
of ART increases the prevalence of hypertension and also increases both systolic and
diastolic blood pressure values. This study comes at a time when the association
between ART and hypertension is ridden with conflicting results although there are
several postulations on the pathophysiology of ART effect on hypertension and CVD
in general. The unfortunate dearth of literature on the causal average treatment effect
of ART on hypertension as observed by Nduka et aI. (2016a) makes it difficult to
compare the present results with previous studies. However, this study is the second in
line in this analysis direction. A study published by Nduka et al. (2016a) reported an
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estimated causal average treatment effect (ATT) of 7.85 mmHg and 7.45 mmHg
increases in systolic and diastolic blood pressure values. The results of the present
study indicate an estimated average treatment effect on the treated (ATT) of 12.0
mmHg on systolic blood pressure and 6.1 mmHg on diastolic blood pressure. Although
the ATT on diastolic blood pressure is comparable to the ATT reported by Nduka et al.
(20 16a), the ATT on systolic blood pressure is higher than the previous study. However,
considering the fact that a 20 mmHg point is needed to move a non-hypertensive
individual maximum threshold systolic blood pressure from 120 mmHg (non-
hypertensive) to 140 mmHg (hypertensive) whilst a 10 mmHg is needed for diastolic
blood pressure (from 80 mmHg to 90 mmHg), the present study's ATT on both systolic
blood pressure and diastolic blood pressure is plausible.
The present study also reported an estimated ATT of 26.2 percent points on
hypertension, which is coherent with conclusions from several studies that is suggestive
of a plausible causal relationship between ART and hypertension and related blood
pressure levels in PLHIV. Reports from the MACS indicated a temporal relationship
between ART and increased blood pressure levels (Seaberg et al., 2005). A systematic
review with meta-analysis involving 39 studies (of varied study designs and settings)
indicated an epidemiological association between ART and increased levels of systolic
and diastolic blood pressure and increased risk of hypertension in PLHIV (Nduka et al.,
2016a).
The interest in the use ofPSM analysis to infer or otherwise a plausible causal effect of
treatment i.e. cause-effect relationships, especially in situations where RCT is not
practicable due to logistical constraints or ethical reasons is growing (Rosenbaum and
Rubin, 1985; Austin, 2011a; Austin, 2011 b). This is consistent with the modifying
concept of causation in epidemiology which has expanded and gone beyond the simple
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relationships established by "Hill's Criteria of Causation" (Hill, 1965; Lucas and
McMichael, 2005; Mirtz et al., 2009) to the use of statistical methods in testing causal
hypothesis (Rosenbaum and Rubin, 1985; Austin, 2011b; Austin, 2011a). To the best
of my knowledge, this study is the second to use propensity score-matched analysis in
examining a plausible causal relationship between ART and blood pressure values in
PLHIV; the first being the study reported by Nduka et al. (2016a).
The role played by ART in elevating blood pressure values and consequently increasing
the risk of hypertension in PLHIV has been attributed to a myriad of interacting patho-
physiological pathways including ART-induced vascular and endothelial dysfunction
(Seaberg et al., 2005; Dau and Holodniy, 2008; Hemkens and Bucher, 2014), an
exaggerated immune response following initiation of ART (Bosamiya, 2011), chronic
inflammation and adipose and liver dysfunction (Hemkens and Bucher, 2014). Worth
noting is that although animal experimental data is lacking in the reversal or preventive
effect of ART on blood pressure (Nduka et al., 2016b), a published clinical trial
demonstrated a significant reduction in blood pressure when patients were switched
from a first generation NNRTI (nevirapine-NVP, in combination with FTC and TDF-
DF) to a second generation NNRTI (rilpivirine-RPV, in combination with FTC and
TDF -DF) (Rokx et al., 2015). It has been suggested that the current use of nevirapine
as a NNRTI in ART is consistent with the rise in the prevalence of hypertension and
associated CVD mortality rates seen since the onset of the ART era (Seaberg et al.,
2005; Hooshyar et al., 2007; Bloomfield et al., 20l4b; Nduka et al., 2016b).
5.6 Cardiovascular risk score assessment
The introduction of ART for the management of HIV has led to a better health care for
PLHIV. However, since the onset of the ART era two decades ago, there is mounting
evidence that both HIV infection and ART are risk factors for the development of
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cardiovascular diseases. In addition to this, most countries in sub-Saharan Africa (SSA)
are undergoing a demographic and epidemiologic transition in terms of disease burden
from infectious diseases to non-communicable diseases and therefore issues have been
raised on the burden of CVD in PLHIV in SSA. Cardiovascular risk scoring systems
have been used to estimate the risk of CVD in the general population and several of
such tools exist as online calculators. The present study estimated the 1O-year FRS, the
5-year D:A:D risk score and the 10-year WHO/ISH risk score in 311 HIV -positive
patients without any previous CVD attending the KBTH in Accra. The present study
estimated a 10-year moderate to high risk of CVD to be 20.6% and 13.2% using the
FRS and the WHO/ISH risk score respectively but D:A:D scored 52.4%.
Cardiovascular disease is a leading cause of death in most population and has been
recognised as an important cause of morbidity and mortality among PLHIV (Reinsch
et aI., 2012). In North America and Europe, CVD is the second most common cause of
death among HIV -infected persons after AIDS-related mortality, hence a shift in long-
term care of PLHIV to include more focused attention on cardiovascular risk
assessment, prevention and appropriate management (Bonnet et al., 2002; Boccara et
aI., 2013; Aberg et aI., 2014). The results of the present study indicate that using the
FRS and WHO/ISH cardiovascular scoring systems, majority of the study participants
were classified as having low risk ofCVD. However, the D:A:D cardiovascular scoring
system classified more than 50% of the study participants to be of moderate to high risk
ofCVD. Although the results of the low risk ofCVD using the FRS and the WHO/ISH
scoring systems are comparable to results of previous studies of 72.3% in Slovenians
(Pirs et aI., 2014), 60.3% in Germans (Reinsch et al., 2012), 93.3% in South Africans
(Mashinya et aI., 2015) and 88.3% in Nigerians (Edward et aI., 2013), there are
arguments on the appropriateness of the use of the FRS in evaluating the cardiovascular
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risk of HIV -positive individuals. Generally, in the HIV -uninfected individuals, it has
been suggested that the FRS overestimates the risk of CVD in Europeans (D'Agostino
et al., 2001; Brindle et al., 2003; Hense et al., 2003). This overestimation of the FRS
have also been observed in HIV-infected Thais (Edwards-Jackson et al., 2011),
Brazilians (Nery et al., 2013) and Europeans (Krikke et al., 2016). This has raised the
issue on the appropriateness of use of the FRS in HIV -infected populations.
However, the present study points to an underestimation of risk of CVD using the FRS
compared with the D:A:D score in this group of HIV -infected Ghanaian population.
This present study estimation of 20.6% moderate to high risk of CVD (FRS) is similar
to studies published by Mashinya et al. (2015), which noted that despite the level of
agreement between the FRS and the D:A:D score in HIV -infected South Africans, the
FRS still underestimates the risk of CVD among PLHIV (Mashinya et al., 2015). The
study further iterated that this underestimation if not taken care of might lead to the
exclusion of individuals who otherwise will benefit from an aggressive CVD risk
prevention or management. The FRS has also been noted to underestimate the risk of
CVD in the general HIV -uninfected South African population (Klug, 2012). This
clearly shows the inappropriateness of using the FRS in sub-Saharan Africa populations
and in particular HIV -infected individuals in this sub-region.
The WHO/ISH prediction chart score is an alternative risk score developed by the WHO
for epidemiological sub-regions but has seldom been used in general and in HIV-
infected populations in particular. The present study report of 86.7% of the study
participants with low risk of CVD using the WHOIlISH prediction chart is comparable
to a study reported in Nigeria of 87.2% (Edward et al., 2013). However, the level of
agreement between the FRS and the WHO/ISH score is over 75.0% (data not shown)
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indicating that it is most probably the WHO/ISH score may also underestimate the level
of risk of CVD among PLHIV in sub-Sahara Africa. The sparse amount of literature in
this direction however makes this argument debatable.
In the present study, whilst all the three cardiovascular scoring systems relatively had
similar scores for individuals with high risk of CVD, the most striking difference
between the systems is in the scoring of individuals with either low or moderate risk of
CVD. The D:A:D scoring system tend to classify more individuals into the moderate
risk class than the FRS and the WHO/ISH risk prediction chart which is similar to
results from other studies (Nery et al., 2013). The important aspect of this group of
patients who are in the moderate risk category is their relatively shorter progression
time to the high-risk group if not managed appropriately because of the HIV -infection
and ART. This makes the D:A:D risk scoring system more appropriate to be used in
HIV infected individuals and especially in patients on ART. In fact, the D:AD score
also indicated that 93.3% and 87.8% of those classified as of high risk or of moderate
risk of CVD respectively were on ART (data not shown). Other studies have noted the
accuracy of using the D:A:D risk score in identifying HIV -positive individuals with
high risk of CVD but a thorough review of this literature has been discussed by
D'Agostino (2011).
5.7 General discussion
With the increasing prevalence of HIV in Ghana and as patients grow older, the
occurrence of hypertension and other cardiovascular risk factors will be seen among
PLHIV especially in patients on ART. In addition, although currently the estimated risk
score of cardiovascular event is low or moderate in most patients, up-scaling of the
CD4+ T cell count threshold for initiation of ART combined with the ageing of the
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population of PLHIV, most patients will eventually fall into the high risk group and
will need management of these cardiovascular events. HIV care however provides a
good opportunity for the management of hypertension and other chronic cardiovascular
events with regular assessment. Regular blood pressure measurement, although
currently routine in the HIV clinic, should be considered an essential element of HIV
care. In addition, studies from South Africa have shown that when non-communicable
diseases care is integrated into HIV care, patients on ART do attain better functional
ability and health status (Nyirenda et al., 2012; Mutevedzi et al., 20l3). Thus, the
current changing pattern of co-morbidities especially cardiovascular diseases among
PLHIV will require health policies and interventions, which may have to incorporate
the integration of chronic disease management into HIV care (Bloomfield et al., 2014b;
Bloomfield et al., 20 14a; Oni et al., 2014). Although, there is paucity of data in the cost-
benefit analysis of such a policy direction, there has been suggestions that integrating
chronic disease management into existing HIV care may in the long term be more cost
effective compared with the traditional vertical approach and single disease
management (Hyle et al., 2014). In fact, such a position is supported by results from a
pilot study at a secondary health facility in Nigeria, which concluded that it is feasible
and therefore a need to integrate cardiovascular screening and management into HIV
care in order to improve life expectancy and sustain gains made in HIV care in the era
of ART (Gwarzo et al., 2012).
5.8 Study limitations
The current study used a cross sectional study design to determine prevalence of
hypertension and associated factors in patients attending HIV clinic at the KBTH in
Accra. Inferences from this study should be done in the context of the study design as
temporality cannot be established. There is no information on the timing of the outcome
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relative to the exposures hence this limits any causal inference including reverse
causation (outcome changing exposure) for some of the factors found to be associated
with hypertension in the regression modelling analysis. However, in an effort to reduce
the effect of reverse causation for HIV IART -related factors, potential study participants
were excluded if they have been diagnosed with hypertension prior to HIV infection
diagnosis/initiation of ART.
In addition, some variables used in the PSM analysis were extracted from the clinical
folders of study participants and hence their appropriateness depends on the extent of
correctness attached to these data when they were collected. Blood pressure readings
were performed at two different clinic days and may be subject of fluctuation secondary
to human and equipment-related factors; however, this was adjusted in the analysis and
any measurement difference would likely be of a non-differential type.
Another limitation of this study is that data were based on measurements taken at one
point in time according to clinical indications and will be assumed to reflect their
chronic condition. Study participant did not receive a definitive diagnosis of
hypertension based on the measurements done, but nonetheless, the 2012 WHO
STEPwise approach to chronic disease risk-factor surveillance instrument was used
(WHO, 2012).
In addition, factors like family history of CVD/hypertension, smoking, alcohol use,
physical inactivity were based on study participants' self-report. Thus it is possible for
respondents to be tempted to present themselves in a more favourable way by giving
health-conscious answers in what is termed "social desirability"(Dimala et al., 2016).
Finally, the study did not include HIV -negative control group for better comparison on
HIV -related factors between HIV -positive individuals and HIV -negative individuals.
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5.9 Study strengths and contribution to knowledge
This is the first study to the best of my knowledge to associate a plausible causal
relationship between ART and increased prevalence of hypertension using propensity
score-matching analysis. In addition, this study is the second to the best of my
knowledge to infer a plausible causal relationship between ART and increased systolic
and diastolic blood pressure values using propensity score-matching analysis. Another
strength of this study is the use of a statistical model (propensity score-matching
analysis) to investigate a potential causal link between ART and blood pressure which
otherwise ethical challenges would have hindered the design of a randomized control
trail to investigate the potential causal link. In addition, the inference of causal link
using estimated ATT reflects a higher level of evidence and a substantial lower risk of
not accounting for residual confounding compared with other observational studies
which used regression modelling techniques (Seeger et al., 2007; Austin, 2011b) .
Another strength of the study is the methods used in ensuring that the selected
covariates in the propensity score-matching sample were sufficiently and appropriately
balanced between the ART-naive group and the ART-exposed group using a
comprehensive three step process; differences in means or proportions, standardised
bias after matching and ratio of variances (Austin, 2011 b). In addition, the covariates
selected in estimation of the propensity score included the risk factors of hypertension
known to persist in the general population.
Also relevant is that this is the first study conducted in Ghana's largest HIV cohort, to
assess the general risk of cardiovascular events among PLHIV using recognised
cardiovascular risk score assessment tools.
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CHAPTER SIX
6.0 CONCLUSIONS. AND RECOMMENDATIONS
6.1 Conclusions
The outcome of this study clearly shows a high prevalence of hypertension in patients
attending the HIV clinic at the KBTH. This observed high prevalence of hypertension
could be due to an "unmasking" of an already high predisposition to hypertension by
the HIV infection itself, the initiation of ARV s and an ageing HIV cohort as figures
from the NACP indicate the 45-49 years' age group have the highest prevalence of HIV
in Ghana. Due to the ongoing debate on the association between ART and hypertension,
a propensity score-matching analysis was employed to examine the average treatment
effect of ART on hypertension and blood pressure values. The present finding
acknowledges ART -associated increase in blood pressure as an entity and results of the
PSM analysis indicate a plausible causal relationship between ART and hypertension
with increases in both systolic and diastolic blood pressure values. The reported
plausible causal link between ART and hypertension/increases in blood pressure
values, which represents a transition from association to causation, could be a
significant step in policy formulation in taking preventive action against hypertension
and its complications among PLHIV. Regression modelling analysis to determine
factors associated with hypertension indicated that the risk factors for hypertension i.e.
increasing age, positive family history of CVD/hypertension, inadequate exercising,
general overweight/obesity and abdominal obesity were associated with hypertension.
In addition, hypercholesterolemia, exposure to ART and duration of this ART exposure
were also found to be associated with hypertension. Finally, using D:A:D risk
assessment system, it was found that over 50% of the individuals who participated in
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the study were having a moderate to high risk of CVD. Outcome from this study
highlights the need to perform cardiovascular risk assessment before initiation of ART
and periodic assessments to ensure prompt detection and management of these risk
factors to prevent the occurrence of cardiovascular events. Although the current NACP
guidelines recognises the possibility of drug-drug interaction between ARVs
administered and some antihypertensive drugs, and adverse events monitoring for
metabolic abnormalities, there is no direction on the systematic screening, prevention
and management of CVD among PLHIV. As the HIV infected cohort grows in age but
cardiovascular events are yet to reach alarming proportions, this presents a rare
opportunity to understand and decrease cardiovascular risk among PLHIV. The results
of this study will help shape policy considering the magnitude of risk of CVD among
PLHIV and the fact that the prevalence of HIV in Ghana is on the ascendency.
6.2 Recommendations
Policy-relevant recommendations
1. A policy framework on CVD risk assessment, prevention and management
in patients attending KBTH HIV clinic should be formulated (including the
use of D:A:D CVD risk assessment tool)-Such preventive measures are
especially crucial for Ghana, where HIV prevalence is increasing, trends in
cardiovascular disease risk factors are rising persistently and antiretroviral
treatment coverage is anticipated to widen.
2. Research into the feasibility of integration of NCD care into HIV -care so
that patients need not seek this care from clinics where they may feel
uncomfortable in disclosing their HIV status to attending healthcare givers.
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Practice-related recommendations
1. Blood pressure monitoring should move from being routine at the KBTH
HIV clinic to more of purposeful screening of patients for hypertension and
patients should be encouraged to have regular blood pressure measurements
at home and not only when they visit the clinic
2. Waist circumference as a marker of abdominal obesity should be regularly
measured in patients attending HIV clinic at the KBTH in Accra.
Further research-related recommendations
1. A longitudinal study is recommended to establish a cohort of patients
attending HIV clinic at the KBTH to understand better the treatment effect
of specific ARVs on specific cardiovascular events.
2. Future research should look at the incorporation HIV -negative controls in
a cohort study for better assessment of effect of HIV status on risk of
cardiovascular events in patients receiving HIV care in Ghana.
3. A systematic review and meta-analysis on the burden of CVD risk factors
in PLHIV specifically in the SSA region is recommended.
4. Usage of PSM analysis should be encouraged in epidemiological studies
involving HIV care in Ghana.
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APPENDICES
APPENDIX I-QUESTIONNAIRE FOR DATA COLLECTION
Hypertension and associated risk factors in patients attending HIV Clinic at the
Korle-Bu Teaching Hospital in Accra
Study ID: InstitutionallD: Date:
Part 1: To be administered to study participant
Sex Age: Educational Level:
Male 0 Female 0 " ...." ....yrs Tertiary 0 Sec./Voc/CommerciaID JSS/Elementary 0 None 0
Religion:
Christian 0 Moslem 0 Traditionalist 0 Atheist 0
Marital Status:
Married 0 Co-habiting 0 Single 0 Divorced 0 Widowed 0
Occupation:
Govt/Private employed 0 Self-employed 0 Unemployed 0 Pensioner 0
Level of income:
............ ./month NoneD
LIFESTYLEFACTORSAND HISTORY
Smoking::
None smoker 0 Current smoker 0 (No. of years ) Previous smoker 0
Alcohol drinking:
None drinker 0 Current drinker 0 (No. of years ) Previous drinker 0
How often do you consume fruits?
Daily 0 /week ......... ./month Rarely 0
How often do you exercise?
Type of Exercise Frequency
Dailvo ......./week ....../month Rarely 0
Dailvri ......./week ....../month Rarely 0
Dailvti ......./week ....../month Rarely 0
Date HIV status was diagnosed? Age at HIV diagnosis:
........................ yrs
Are you currently on ARVs:
Yes 0 No 0
Do you have any of these diseases? Is any family member with any of these diseases?
Hypertension 0 Year of diagnosis . Hypertension 0 Relation." " " .
Diabetes 0 Year of diagnosis ...." ......... ". Diabetes 0 Relation." " .
Hyperlipidemia 0 Year of diagnosis .." ...." ...... " Hyperlipidemia 0 Relation .
Other Year of diagnosis " .. Others (specify)
Other Year of diagnosis .." ..." ...... ".
Other Year of diagnosis .." ....." ....".
How does the presence of this/these co-morbidity (ies) affect you?
ANTHROPOMETRIC MEASUREMENTS
Weight (kg):
1st: 2nd:
Waist Circumference (m): Hip circumference (m):
1st: 2nd: 1st: 2nd:
1st: 2nd:
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Hypertension and associated risk factors in patients attending HIV Clinic at the
Korle-Bu Teaching Hospital in Accra
Study 10: Institutional 10: Date:
Part 2: To be extracted from clinical folder
Height (m): Date HIV status was diagnosed: Age at HIV diagnosis (yrs):
HIV stage at diagnosis: Viral load at diagnosis:
Stage 10 Stage 2 0 Stage 3 0 Stage 4 0
HIV type: CD. count at diagnosis (cells/ml):
HIV 10 HIV 2 0 Mixed 0
History of ART administered
ARVadministered Date started Date stopped Reason(s) for HAART Modification
Co-morbid conditions
Hypertension 0 Year of diagnosis ................ 1. Other Year of diagnosis .............
Diabetes 0 Year of diagnosis ................. 2. Year of diagnosis ............
Hyperlipidemia 0 Year of diagnosis ................. 3. Year of diagnosis .............
Tuberculosis 0 Year of diagnosis ................. 4. Year of diagnosis .............
Concomitant medications Adverse events (for patients on ARVs)
1. 1. 6.
2. 2. 7.
3. 3. 8.
4. 4. 9.
5. 5. 10.
Parameters (Within the last 1year) Parameters (Most current)
Parameter Date Value Parameter Date Value
BP Viral load
BP Haemoglobin level
BP AST
BP ALT
BP Urea
Creatinine
Weight Fasting Blood glucose
Weight
Weight Nadir CD4 Count
Weight Others:
Weight BPat enrolment
CD4 at enrolment
CD4 Count Weight at enrolment
CD4 Count
CD4 Count
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APPENDIX II-PATIENT CONSENT FORM
PATIENT CONSENT FORM
This form is to be read individually and privately to each participant recruited into the
study. Following consent to participate in the study, each patient is to be asked to give
documented consent
RESEARCH TITLE
Hypertension and associated risk factors in patients attending HIV clinic at the
Korle-bu Teaching Hospital in Accra
Principal Investigator: Edmund Tetteh Nartey
Address: Department of Epidemiology, School of Public Health, University of
Ghana
Centre for Tropical Clinical Pharmacology and Therapeutics, University
of Ghana
Introduction
This Consent Form contains information about the research named above. In order to
be sure that you are informed about being in this research, we are asking you to read
(or have read to you) this Consent Form. You will also be asked to sign it (or make your
mark in front of a witness). We will give you a copy of this form. This consent form
might contain some words that are unfamiliar to you. Please ask us to explain anything
you may not understand.
Reason for the Research
You are being asked to take part in research to find out about the number of people with
hypertension and whether the medicines being given to you (ARV) are responsible for
the hypertension
Research Purpose/General Information about Research
This is a research to find out whether the HIV in persons on ARV (or on care) is
resulting in these patients getting hypertension. In addition, whether other factors like
age, sex and the drugs (ARVs) they are taking is resulting in hypertension. The
development of hypertension and other chronic diseases like diabetes can make the
management of the HIV disease challenging. Some people may have higher possibility
of developing hypertension and other chronic conditions than others. This research will
find out what makes some PLHIV to develop hypertension and look at their liver
enzymes, kidney function, blood sugar level as well as their CD4 count.
Your Part in the Research
If you agree to be in the research, you will be asked to answer a few question on your
exercises, fruit consumption etc and also asked to give 7ml of your blood sample, taken
from your veins, only once, although the project is expected to last over a two-year
period. About 310 adults (both males and females) from KBTH HIV clinic will take
part in this study.
Possible Risks and Discomforts
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Participants may have some discomfort and pain when blood sample is being taken.
However, efforts will be made to minimize any risk and reduce discomfort associated
with this procedure according to laid down rules in good laboratory practices.
Possible Benefits
The data generated from this research would be useful in monitoring your CD4 count
and the presence of other diseases in you including liver and kidney diseases. This will
directly benefit participants by informing change in treatment regimens if necessary or
advice on health style. The project will also benefit all persons living with HIV by
providing information for the better management of the infection.
Alternatives to Participation
You will continue to benefit from the ART programme even if you decline to participate
in this research project
Confidentiality
We will protect information about you and your part in this research to the best of our
ability. No report generated from this research will name, or be linked to, the persons
involved. Where necessary however, the researcher and your doctor may need to
discuss your specific results to enable a better management of your condition.
Compensation
You will not be paid for participating in this research
Your rights as a participant
This research project has been reviewed and approved by the EPRC of the College of
Health Sciences, University of Ghana. If you have any questions about your rights as
a research participant you may contact The Research Office of the School of Medicine
and Dentistry, University of Ghana located on the Korle-bu Campus.
If You Have a Problem or Have Other Questions, please call Edmund Nartey (0244
220014)
228
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APPENDIX III-STUDY PARTICPANTS AGREEMENT FORM
The above document describing the benefits, risks and procedures for the research title
"Hypertension and associated risk factors in patients attending HIV clinic at the Korle-
bu Teaching Hospital" has been read and explained to me. I have been given an
opportunity to have any questions about the research answered to my satisfaction. I
agree to participate as a volunteer.
Date Signature or mark of volunteer
If volunteers cannot read the form themselves, a witness must sign here:
I was present while the benefits, risks and procedures were read to the volunteer. All
questions were answered and the volunteer has agreed to take part in the research.
Date Signature of Witness
I certify that the nature and purpose, the potential benefits, and possible risks associated
with participating in this research have been explained to the above individual.
Date Signature of Person Who Obtained Consent
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APPENDIX IV-ETHICAL CLEARANCE CERTIFICATE
UNIVERSITY OF Of-lANA
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