Abstract
Background:
The effect of hypertension is aggravated by lifestyle factors such as alcohol consumption. This study sought to determine the association between alcohol consumption and the level of blood pressures among HIV seronegative and seropositive cohorts.
Methods:
This secondary analysis was performed on a cross-sectional survey data of 17 922 participants during the period between 2018 and 2020. A questionnaire was used to obtain participants’ alcohol consumption history, which was categorized into non-alcohol consumers, non-heavy alcohol consumers, and heavy alcohol consumers. A linear regression model was used to establish relationships among participants with raised blood pressure (BP ≥ 140/90 mmHg).
Results:
Out of the total participants, 3553 (19.82%) were hypertensives. Almost 13% of the hypertensives (n = 458; 12.89%) were undiagnosed, and 12.44 % (442) had uncontrolled hypertension. About 14.52% of the hypertensives (3553) were not on any antihypertensive medication. Male non-consumers of alcohol had the highest systolic and diastolic BP; uncontrolled systolic BP (165.53 ± 20.87 mmHg), uncontrolled diastolic BP (102.28 ± 19.21mmHg). Adjusted for covariates, moderate alcohol consumption was associated with HTN among participants who were HIV seropositive [unadjusted (RR = 1.772, P = .006, 95% CI (1.178-2.665)], [RR = 1.772, P = .005, 95% CI (1.187-2.64)]. [unadjusted RR = 1.876, P = .036, 95% CI (1.043-3.378)], adjusted RR = 1.876, P = .041, 95% CI (1.024-3.437). Both moderate and heavy alcohol consumption were significantly related to hypertension among HIV sero-negative [unadjusted model, moderate consumption RR = 1.534 P = .003, 95% CI (1.152-2.044)], [adjusted model, moderate alcohol consumption RR = 1.535, P = .006, 95% CI (1.132-2.080)], [unadjusted model, heavy alcohol consumption, RR = 2.480, P = .030, 95% CI (1.091-5.638)], [adjusted model RR = 2.480, P = .034, 95% CI (1.072-5.738)].
Conclusion:
Alcohol consumption is significantly related to increase BP regardless of HIV infection.
Keywords: human immunodeficiency virus (HIV), undetected hypertension, heavy alcohol consumption, uncontrolled hypertension
Introduction
The burden of infectious diseases such as human immunodeficiency virus (HIV) disease, tuberculosis (TB), and the recent SAR Cov 2 in sub-Sahara Africa (SSA) calls on scientists and policymakers to intensify the need to reduce emerging non-communicable diseases (NCD). It is equally imperative to understand the epidemiology of risk factors for NCD in the context of geography and socioeconomic status to offer unique interventions in mitigating the burden of these diseases. For the past decade, SSA has seen a rise in cardio-metabolic disease (CMD).1 -6 The increasing burden of CMD in SSA is related to the epidemiological transition in the region which is mostly driven by factors such as unhealthy lifestyles including alcohol consumption, cigarette smoking, physical inactivity, and unhealthy diet, among other emerging risks.2,5,7
Alcohol consumption can have both positive and negative effects on the body. Low-to-moderate alcohol consumption may minimize the risk of atherosclerotic cardiovascular diseases (ASCD) which can lead to coronary artery disease (CAD) and stroke. On the other hand, excessive alcohol consumption can cause major physiological effects such as alterations in circulation, oxidative stress, inflammatory response, mitochondrial dysfunction, programmed cell death, as well as structural harm to the cardiovascular system, particularly the heart muscle itself.
The relationship between alcohol consumption and hypertension has been largely explored. In healthy individuals, low to moderate daily alcohol consumption typically has no immediate or noticeable effects on hemodynamics or blood pressure (BP). But binge drinking, which is defined as having more than 5 standard drinks in 1 sitting, has been associated with momentary increases in blood pressure ranging from 4 to 7 mmHg for systolic blood pressure and 4 to 6 mmHg for diastolic blood pressure.8,9
Halanych et al, 10 examined the relationship between alcohol consumption and incidence of hypertension (systolic BP ≥140 mmHg/diastolic BP ≥90 mmHg or antihypertensive medication use) among participants in the CARDIA study. Six categories of alcohol consumption were identified: non consumers (never consumed alcohol at baseline), former consumers (never consumed alcohol in the previous year), light consumers (consumed fewer than 7 drinks per week for men and fewer than 4 drinks per week for women), moderate consumers (consumed 7-14 drinks per week for men and 4-7 drinks per week for women), and at-risk drinkers (consumed more than 14 drinks per week for men and more than 7 drinks per week for women). After adjusting for variables such as age, gender, body mass index (BMI), cigarette smoking status, a family history of hypertension, and other socioeconomic characteristics, it was shown that alcohol consumption was often not associated with a 20-year incidence of hypertension, the incidence of hypertension was lower among European-American women who had ever consumed alcohol. 10
Even after adjusting for ART use, CD4 count, and traditional cardiovascular (CVD) risk factors, harmful alcohol consumption, abuse, and dependence were associated with higher prevalence of CVD among men living with HIV (PLWH), whereas past consumers of alcohol had a higher prevalence of CVD among men without HIV. 11 There are available literature on the prevalence of alcohol consumption among PLWH, and the prevalence of CVD related disease among but there is paucity of literature addressing the intersection of alcohol consumption and CVD among PLWH in the contest of SSA where genetic is key in determining the relationship.
These inconsistencies in output necessitate a regional epidemiological study to examine the effect of alcohol consumption on blood pressure levels among both men and women who are HIV sero-positive and negative.
We therefore analyzed observational data by the Vukuzazi team to assess the effect of alcohol consumption on hypertension among HIV sero-negative and positive participants. We hypothesized that alcohol consumption would not be associated with hypertension in both HIV sero-negative and positive.
Material and Methods
Study Setting, Study Design, and Recruitment
This study was a secondary analysis of an 18-month (between 2018 and 2020) observational study done by the Vukuzazi team. 11 The materials and methods have been described in earlier publications.11,12 Out of 39 000 eligible individuals, a total of 18, 024 participants completed the study questionnaire. For the completeness of data on the stated objective, 17 922 participants out of the total population was used for this secondary analysis. Study participants were individuals aged 15 years and older who were residents of the uMkhanyakude district of KwaZulu-Natal as at the time of the data collection. 12
Ethical Consideration
Ethical approval of the original work was received from the Ethics Committees of the University of KwaZulu-Natal, the London School of Hygiene and Tropical Medicine, the Partners Institutional Review Board, and the University of Alabama at Birmingham whilst the current analysis received its ethical approval from both the Africa Health Research Institute Institutional Review Board and the University of Limpopo with a project number TREC/112/2021: IR. Permission was also obtained from the Vukuzazi team to access the database for the secondary analysis.
Invitation Process at the Participants’ Homestead and Informed Consent
The current study was solely based on the secondary analysis of data from the Vukuzazi program, therefore informed consent was waived on behalf of the informed consent obtained during data acquisition from the Vukuzazi team. Details of the invitation is found in the study published by the Vukuzazi team 12 and our previous publication. 13
Field and Laboratory Procedures
At the Vukuzazi camp, research nurses administered questionnaires to assess the individual’s history of HIV, hypertension, and diabetes mellitus. 12 History of alcohol consumption was self-reported. Anthropometric and blood pressure measurements were done using the WHO STEPS protocol.
Study Variables, Measurements, and Their Definitions
Outcome variable
The outcome variable measured in this study was hypertension. Using the WHO stepwise protocol, both systolic and diastolic blood pressure (SBP and DBP) readings were measured using portable electronic devices (OMRON Healthcare, Model M6). 12 After 30 minutes of inactivity, 3 blood pressure (BP) measurements, with 5 minutes resting time in between were taken. 12
Hypertension was operationally defined as participants who had systolic blood pressure (SBP) ≥140 mmHg and/or diastolic blood pressure (DBP) ≥90 mmHg at the time of data collection14,15 and/or participants who were on antihypertensive medications. Uncontrolled hypertension were participants who were on antihypertensive medication, but their BP measurements were higher than 140/90 mmHg while undiagnosed hypertension was defined as participants who had raised SBP and DBP above 140/90 mmHg but were not aware that they are hypertensives and had not yet started any antihypertensive treatment.
Predictor variables
The predictor variables measured in this study were grouped as;
Background characteristics of study participants.
Biological risk factors of study participants
Behavioral risk factors of study participants
Type 2 diabetes mellitus of study participants
These predictor variables were selected at priori based on previous studies.
Exposure variable
Alcohol consumption history was categorized into non-alcohol consumers, non-heavy alcohol consumers, and heavy-alcohol consumers. History of alcohol consumption was used as the exposure variable. Alcohol consumption was assessed by the use of self-reported questionnaire. Details of the categorization can be found in our previous publication. 13
Background characteristics of study participants
The background characteristics of study participants consisted of age and gender. Age was categorized at priori using existing studies on hypertension in the sub-Sahara. Age was categorized into 3 groups [young adults (15-25 years), middle adults (26-50 years), and elderly (>50 years)]. Gender was measured binary as male and female.
Biological risk factors of study participants
The biological risk factors of study participants included body mass index (BMI), and waist-to-hip-ratio (WHR). Anthropometric measurements: weight, height, and waist circumference were taken from study participants. The BMI was also calculated as weight of patients in kilograms divided by the square of the height in meters. 13
The behavioral risk factors of study participants
Behavioral risk factors measured in this study were cigarette smoking and alcohol consumption history. The categorization of smoking history was described in our previous publication. 13
HIV Status and history of type-2 diabetes mellitus of study participants
During the process of sampling at the camp, research nurses administered questionnaires to assess the participants’ history of HIV, hypertension, and diabetes. Blood samples were collected for the measurement of glycosylated hemoglobin (glycated hemoglobin; [HbA1c]), using the VARIANT IITURBO Hemoglobin testing system [Bio-Rad, Marnesla-Coquette, Paris, France]) while HIV was tested using an immunoassay (Genscreen Ultra HIV Ag-Ab enzyme immunoassay [Bio-Rad]). Participants who tested positive to the HIV ELSIA were further assessed for viral load quantification (Abbott Real Time HIV-1 Viral Load [Abbott, IL, USA]). Details of these had been published in the original study by the Vukuzazi team.13,16
Statistical Analysis
Data was analyzed with STATA/SE version 14.2. Descriptive analysis such as frequencies, and percentages, were used to describe the study population. The primary exposure of interest in this analysis was alcohol consumption. Covariates included history of smoking, obesity, gender, age, and diabetes mellitus disease cascade which were treated as potential confounders. The null hypothesis was that alcohol consumption was not significantly related to an increase in both the systolic and diastolic pressures among participants who are either HIV sero-negative or positive.
Chi-square was used to analyze relationships between background characteristics stratified by participants’ HIV sero status. Multinomial logistic regression was used to analyze relationships between systolic and diastolic pressures and predictor variables while controlling for confounding variables. The results were reported as relative risk ratio (RRR) and its respective 95% confidence intervals (95% CI). The final fitted model was built adjusting for age and gender and hypertension risk factors reported in literature. P value <.05 were considered statistically significant.
Results
A total of 17 922 participants were considered for the analysis. Figure 1 shows the prevalence of hypertension among the cohort. Out of the total of 17 922 participants, 3553 (19.82%) were hypertensive while 14 396 (80.18%) were non-hypertensive.
Figure 1.
Hypertension prevalence among participants.
From Figure 2, out of the 3553 hypertensive participants, 442 (12.44%) had uncontrolled hypertension (HTN) at the time of data collection, while 2595 (87.56%) had normal BP (controlled HTN).
Figure 2.
Prevalence of uncontrolled hypertension among the participants.
Figure 3 shows the prevalence of undiagnosed HTN among the participants; 458 (12.89%) of the hypertensives were undiagnosed HTN or unaware that they were hypertensives.
Figure 3.
Prevalence of undiagnosed hypertension among participants.
Figure 4 shows that 112 (24.45%) of the hypertensives were HIV sero-positive while 342 (74.67%) were HIV sero-negative. Antihypertensive treatment use among the hypertensives is shown in Figure 5; 516 (14.52 %) of the 3553 hypertensives were not on any antihypertensive medication.
Figure 4.
HIV status of participants.
Figure 5.
Use of antihypertensive medication among the hypertensive participants.
Table 1 shows the background characteristics of the participants stratified by their alcohol consumption history. Majority (n = 2848; 93.77%) of the hypertensive participants were non-consumers of alcohol, 174 (5.7%) were moderate consumers of alcohol, and only 15 (0.53%) were heavy consumers of alcohol.
Table 1.
Stratification of Background Characteristics and HTN Disease Cascade of Participants Based on Their Alcohol Consumption History (N = 3037).
| Background characteristics | Non-consumers (N = 2848) | Moderate consumers (N = 174) | Heavy consumer(N = 15) | |||
|---|---|---|---|---|---|---|
| HTN-uncontrolled N(%) | HTN-controlled N (%) | HTN-uncontrolled N (%) | HTN-controlled N (%) | HTN-uncontrolled N(%) | HTN-controlled N (%) | |
| Gender | ||||||
| Male | 142 (34.63) | 751 (30.80) | 10 (32.26) | 47 (32.87) | 4 (28.57) | |
| Female | 268 (65.37) | 1687 (69.20) | 21 (67.74) | 96 (67.13) | 1 (100.00) | 10 (71.43) |
| Total | 410 (100.00) | 2438 (100.00) | 31 (100.00) | 143 (100.00) | 1 (100.00) | 14 (100.00) |
| Age (years) | ||||||
| 15-25 | 225 (54.88) | 1264 (51.85) | 18 (58.06) | 80 (55.94) | 9 (64.29) | |
| 26-50 | 126 (30.73) | 809 (33.18) | 11 (35.48) | 48 (33.57) | 1 (100.00) | 4 (28.57) |
| Above 50 | 59 (14.39) | 365 (14.97) | 2 (6.45) | 15 (10.49) | 1 (7.14) | |
| Total | 410 (100.00) | 2438 (100.00) | 31 (100.00) | 143 (100.00) | 1 (100.00) | 14 (100.00) |
| BMI | ||||||
| Underweight | 4 (1.00) | 23 (0.97) | 1 (3.45) | 10 (7.30) | 1 (7.14) | |
| Normal | 46 (11.53) | 390 (16.42) | 8 (27.59) | 62 (45.26) | 7 (50.00) | |
| Overweight | 90 (22.56) | 554 (23.33) | 8 (27.59) | 31 (22.63) | 1 (100) | |
| Obese | 259 (41.38) | 1408 (59.28) | 12 (41.38) | 34 (24.82) | 6 (42.86) | |
| Total | 339 (100.00) | 2375 (100.00)) | 29 (100.00) | 137 (100.00) | 1 (100.00) | 14 (100.00) |
| Waist-hip-ratio | ||||||
| Normal | 131 (131) | 768 (31.70) | 13 (41.94) | 41 (28.87) | 6 (42.86) | |
| Abdominal obesity | 276 (67.81) | 1655 (68.30) | 18 (58.06) | 101 (71.13) | 1 (100.00) | 8 (57.14) |
| Total | 407 (100.00) | 2423 (100.00) | 31 (100.00) | 142 (100.00) | 1 (100.00) | 14 (100.00) |
| Smoking history | ||||||
| Non-smokers | 401 (98.54) | 2395 (98.24) | 19 (61.29) | 94 (65.73) | 1 (100.00) | 10 (71.43) |
| Current smokers | 5 (1.22) | 25 (1.03) | 12 (38.71) | 36 (25.17) | 3 (21.43) | |
| Ex-smokers | 1 (0.24) | 18 (0.74) | 13 (9.09) | 1 (7.14) | ||
| Total | 410 (100.00) | 2438 (100.00) | 31 (100.00) | 143 (100.00) | 1 (100.00) | 14 (100.00) |
| DM type 2 | ||||||
| Yes | 137 (33.41) | 792 (32.58) | 5 (16.13) | 22 (15.38) | 1 (7.14) | |
| No | 273 (66.59) | 1639 (67.42) | 26 (83.87) | 121 (84.62) | 1 (100.00) | 13 (92.86) |
| Total | 410 (100.00) | 2431 (100.00) | 31 (100.00) | 143 (100.00) | 1 (100.00) | 14 (100.00) |
Abbreviations: Unless otherwise stated, HTN, hypertension; HSP, HIV sero-positive; HSN, HIV sero-negative; N, number of observations; %, percentage of distribution.
Table 2 shows the mean and standard deviation of participants’ SBP stratified by their alcohol consumption history. Among the non-consumers of alcohol, males had the highest systolic BP [uncontrolled (165.53 ± SD = 20.87 mmHg), while females had the highest uncontrolled hypertension among the moderate consumers (161.14 mmHg ± SD = 22.15 mmHg).
Table 2.
Mean Distribution of Participants Systolic Blood Pressure Stratified by History of Alcohol Consumption.
| Background characteristics | Non-consumers (N = 2848) | Moderate consumers (N = 174) | Heavy consumer(N = 15) | |||
|---|---|---|---|---|---|---|
| HTN-uncontrolled m (SD) | HTN-controlled m (SD) | HTN-uncontrolled m (SD) | HTN-controlled m (SD) | HTN-uncontrolled m (SD) | HTN-controlled m (SD) | |
| Gender | ||||||
| Male | 165.53 (20.87) | 127.89 (16.81) | 156.5 (16.63) | 131.57 (14.62) | 127 (16.10) | |
| Female | 161.28 (16.87) | 127.62 (16.50) | 161.14 (22.15) | 127.53 (18.39) | 182 | 130.69 (12.54) |
| Age (years) | ||||||
| 15-25 | 163.01 (19.54) | 128.65 (17.18) | 154.89 (11.96) | 130.19 (18.22) | 131.55 (12.83) | |
| 26-50 | 162.51 (17.87) | 126.80 (15.97) | 169.18 (28.89) | 126.77 (16.82) | 182 | 132.25 (5.5) |
| Above 50 | 162.34 (15.32) | 126.43 (15.68) | 150 (8.48) | 128.39 (13.66) | 102 | |
| BMI a | ||||||
| Underweight | 158.75 (18.96) | 122.74 (14.46) | 166 | 122 (10.40) | 143 | |
| Normal | 161.06 (16.79) | 127.03 (17.54) | 152.75 (12.57) | 127.76 (18.67) | 126.43 (12.92) | |
| Overweight | 163.19 (17.72) | 127.85 (16.55) | 157 (15.66) | 129.13 (18.88) | 182 | |
| Obese | 162.33 (18.25) | 127.77 (16.240) | 166.83 (27.48) | 131.62 (15.37) | 131.16 (13.90) | |
| Waist-hip-ratio b | ||||||
| Normal | 158.86 (14.95) | 126.98 (16.50) | 155.54 (14.10) | 127.45 (16.38) | 130.14 (14.62) | |
| Abdominal obesity | 164.26 (19.25) | 127.81 (16.57) | 162.61 (23.86) | 130.48 (18.32) | 182 | 129.14 (12.56) |
| Smoking history | ||||||
| Non-smokers | 162.91 (18.46) | 127.67 (16.60) | 164.05 (23.56) | 128.55 (19.12) | 182 | 128.10 (13.16) |
| Current smokers | 154.80 (14.87) | 129.80 (16.72) | 152.67 (11.66) | 129.19 (14.07) | 130.33 (15.31) | |
| Ex-smokers | 141 | 129.27 (15.31) | 130.15 (11.21) | 143 | ||
| DM type 2 | ||||||
| Yes | 163.56 (19.41) | 128.79 (16.92) | 165.19 (38.25) | 138.04 (15.15) | 138 | |
| No | 162.35 (17.96) | 127.19 (16.43) | 158.57 (16.00) | 127.19 (17.19) | 182 | 129 (13.41) |
Abbreviations: Unless otherwise stated, HTN, hypertension; HSP, HIV sero-positive; HSN, HIV sero-negative; N, number of observations; %, percentage of distribution.
145 missing, b61 missing.
Among participants who were non-consumers, the highest BP was observed among participants who were young adults 15 to 25 years [uncontrolled (163.01 ± 19.54 mmHg), controlled (128.65 mmHg ± 17.18 mmHg)] but among the moderate consumers, the highest BP was seen among the age group (26-50 years) [uncontrolled 169.18 ± 28.89 mmH)]. Participants who were overweight and non-consumers recorded the highest mean SBP [uncontrolled (163.19 ± 17.72 mmHg)], while participants who were obese and moderate consumers of alcohol recorded the highest SBP [uncontrolled 166.83 ± 27.48 mmHg). Among participants who were non-consumers and moderate consumers of alcohol, and those who had abdominal obesity had the highest SBP [uncontrolled (164.26 ± 19.25 mmHg)] and [uncontrolled (162.61 mmHg ± 23.86 mmHg)] respectively.
Table 3 shows the mean and standard deviation of participants’ DBP. Among the uncontrolled hypertensives who were non-consumers and moderate consumers of alcohol, males had the highest DBP (102.28 ± 19.21 mmHg). Among the uncontrolled hypertensives who were non-consumers of alcohol, the highest DBP was observed among participants who were aged 15 to 25 years (100.94 mmHg ± 15.72); however, among the moderate consumers of alcohol, the highest DBP was seen among the middle age participants (26-50 years; 98.82 ± 5.55 mmHg). Participants who were underweight and non-consumers of alcohol recorded the highest mean DBP (103.75 ± 8.54 mmHg) while participants who were obese and moderate consumers of alcohol recorded the highest DBP (99.42 ± 6.05 mmHg). Participants with abdominal obesity who were non-consumers of alcohol had the highest DBP (99.93 ± 10.26 mmHg).
Table 3.
Mean Distribution of Participants Diastolic Blood Pressure Stratified by History of Alcohol Consumption.
| Background characteristics | Non-consumers (N = 2848) | Moderate consumers (N = 174) | Heavy consumer (N = 15) | |||
|---|---|---|---|---|---|---|
| HTN-uncontrolled m (SD) | HTN-controlled m (SD) | HTN-uncontrolled m (SD) | HTN-controlled m (SD) | HTN-uncontrolled m (SD) | HTN-controlled m (SD) | |
| Gender | ||||||
| Male | 102.28 (19.21) | 76.99 (8.89) | 97 (3.19) | 77.57 (10.19) | 72.75 (9.88) | |
| Female | 99.03 (7.97) | 76.54 (9.33) | 98.04 (6.078) | 76.60 (9.04) | 93 | 81.30 (9.03) |
| Age (years) | ||||||
| 15-25 | 100.94 (15.72) | 77.00 (9.25) | 97.61 (5.21) | 72.50 (10.21) | 80.33 (9.34) | |
| 26-50 | 98.82 (8.49) | 76.51 (9.25) | 98.82 (5.55) | 71.29 (10.07) | 93 | 80.25 (7.18) |
| Above 50 | 100.03 (9.47) | 75.92 (8.87) | 92.5 (.71) | 70.32 (10.06) | 60 | |
| BMI | ||||||
| Underweight | 103.75 (8.54) | 78.17 (10.82) | 97 | 76.69 (11.35) | 89 | |
| Normal | 100.33 (19.25) | 75.83 (9.67) | 97 (5.63) | 75.45 (8.79) | 76.71 (10.90) | |
| Overweight | 99.04 (8.96) | 75.82 (8.99) | 96.62 (4.59) | 75.25 (10.76) | 93 | |
| Obese | 99.79 (10.43) | 77.11 (9.03) | 99.42 (6.05) | 80.23 (8.30) | 79.67 (8.75) | |
| Waist-hip-ratio | ||||||
| Normal | 99.57 (13.51) | 76.44 (9.17) | 97.46 (5.08) | 76.72 (9.24) | 77.85 (11.69) | |
| Abdominal obesity | 99.93 (10.26) | 76.70 (9.20) | 97.88 (5.57) | 77.01 (9.65) | 93 | 79.86 (8.17) |
| Smoking history | ||||||
| Non-smokers | 100.21 (13.16) | 80.16 (8.98) | 96.89 (5.33) | 78.36 (8.94) | 93 | 79 (14.73) |
| Current smokers | 97.40 (7.54) | 76.17 (7.29) | 99 (5.17) | 77.69 (7.28) | 89 | |
| Ex-smokers | 93 | 76.65 (9.20) | 76.26 (9.84) | 77.80 (8.74) | ||
| DM type 2 | ||||||
| Yes | 99.47 (9.05) | 76.47 (8.85) | 98.19 (6.22) | 78.45 (7.46) | 84 | |
| No | 100.50 (14.69) | 76.82 (9.35) | 97.61 (5.22) | 76.64 (9.71) | 93 | 78.46 (10.03) |
Abbreviations: Unless otherwise stated, HTN, hypertension; m, mean of distribution; SD, standard deviation.
Table 4 shows association between alcohol consumption categories and the outcome variable which is HTN. Adjusted for covariates, moderate alcohol consumption was associated with HTN among participants who were HIV seropositive [unadjusted RR = 1.772, P = .006, 95% CI (1.178-2.665)], [adjusted RR = 1.772, P = .005, 95% CI (1.187-2.64)].
Table 4.
Multinomial Logistic Regression of HTN Participants Who Were HIV Sero-Positive.
| Hypertension | Model 1-unadjusted | Model 2-adjusted | ||||||
|---|---|---|---|---|---|---|---|---|
| RR | P > t | 95% conf. Interval | RR | P > t | 95% conf. interval | |||
| BP < 140 (base outcome). | ||||||||
| Alcohol consumption history | ||||||||
| Non-alcohol consumers | Ref | Ref | Ref | Ref | Ref | Ref | Ref | Ref |
| Moderate consumers | 1.772 | .006* | 1.178 | 2.665 | 1.772 | .005* | 1.187 | 2.647 |
| Heavy consumers | 0.537 | .547 | 0.071 | 4.061 | 0.537 | .562 | 0.066 | 4.389 |
| _cons | 0.511 | .235 | 0.168 | 1.549 | 0.511 | .256 | 0.160 | 1.626 |
Abbreviations: RR, relative risk; CI, confidence interval; ART, antiretroviral therapy; _cons: constant; Ref: reference.
Unadjusted model: Number of obs = 6040 LR χ2 (11) = 217.95 Prob > χ2 = .0000 Pseudo R2 = .1083. Adjusted: Number of obs = Wald χ2 (13) = 255.20 Prob > χ2 = .0000 Pseudo R2 = .1083. Analysis was conducted by multinomial logistics regression model with 2 models. Model 1 is unadjusted. Model 2 adjusted for gender, age, BMI, WHR, smoking history, type 2 diabetes mellitus, and HTN medication.
P < .05 was considered statistically significant. Multivariate logistic regression was used to obtain values.
When ART medication and duration is factored in the model as shown in Table 5, moderate alcohol consumption was significantly associated with HTN, [unadjusted RR = 1.876, P = .036, 95% CI (1.043-3.378)], [adjusted RR = 1.876, P = .041, 95% CI (1.024-3.437)].
Table 5.
Multinomial Logistic Regression of HTN Participants Who Were HIV Sero-Positive.
| Hypertension | Model 1-unadjusted | Model 2-adjusted | ||||||
|---|---|---|---|---|---|---|---|---|
| RR | P > t | 95% conf. interval | RR | P > t | 95% conf. tnterval | |||
| BP < 140 (base outcome). | ||||||||
| Alcohol consumption history | ||||||||
| Non-alcohol consumers | Ref | Ref | Ref | Ref | Ref | Ref | Ref | Ref |
| Moderate consumers | 1.876 | .036* | 1.043 | 3.378 | 1.876 | .041* | 1.024 | 3.437 |
| Heavy consumers | 2.06e-06 | .987 | — | — | 2.06e-06 | .000 | 9.73e-07 | 4.38e-06 |
| _cons | 1.707 | .527 | 0.325 | 8.956 | 1.707 | .536 | 0.314 | 9.268 |
Abbrevitations: RR, relative risk; CI, confidence interval; ART, antiretroviral therapy; _cons, constant; Ref, reference.
Unadjusted model: Number of obs = 3654 LR χ2(11) = 139.32 Prob > χ2 = .1279 Pseudo R2 = .0249. Adjusted: Number of obs = 3654 Wald χ2(13) = 1381.92 Prob > χ2 = .0000 Pseudo R2 = .1279. Analysis was conducted by multinomial logistics regression model with 2 models. Model 1 is unadjusted. Model 2 adjusted for gender, age, BMI, WHR, smoking history, type 2 diabetes mellitus, ART use, and ART duration (in years).
P < .05 was considered statistically significant. Multivariate logistic regression was used to obtain values.
From Table 6 below, both moderate and heavy alcohol consumption were significantly associated with HTN unadjusted model, moderate consumption [RR = 1.534, P = .003, 95% CI (1.152-2.044)], adjusted model, moderate alcohol consumption [RR = 1.535, P = .006, 95% CI (1.132-2.080)], unadjusted model, heavy alcohol consumption, [RR = 2.480, P = .030, 95% CI (1.091-5.638)], adjusted model [RR = 2.480, P = .034, 95% CI (1.072-5.738)].
Table 6.
Multinomial Logistic Regression of HTN Participants Who Were HIV Sero-Negative.
| Hypertension | Model 1-unadjusted | Model 2-adjusted | ||||||
|---|---|---|---|---|---|---|---|---|
| RR | P > t | 95% conf. intervaL | RR | P > t | 95% conf. interval | |||
| BP < 140 (base outcome). | ||||||||
| Alcohol consumption history | ||||||||
| Non-alcohol consumers | Ref | Ref | Ref | Ref | Ref | Ref | Ref | Ref |
| Moderate consumers | 1.534 | .003* | 1.152 | 2.044 | 1.535 | .006* | 1.132 | 2.080 |
| Heavy consumers | 2.480 | .030* | 1.091 | 5.638 | 2.480 | .034* | 1.072 | 5.738 |
| _cons | 0.036 | .000 | 0.019 | 0.071 | 0.036 | .000 | 0.018 | 0.074 |
Abbreviations: RR, relative risk; CI, confidence interval; ART, antiretroviral therapy; _cons, constant; Ref, reference.
Unadjusted model: Number of obs = 11 627 LR χ2(11) = 478.64 Prob > χ2 = .0000 Pseudo R2 = .0919. Adjusted: Number of obs = 11 627 Wald χ2(13) = 551.93 Prob > χ2 = .0000 Pseudo R2 = .0919. Analysis was conducted by multinomial logistics regression model with 2 models. Model 1 is unadjusted. Model 2 adjusted for gender, age, BMI, WHR, smoking history, type 2 diabetes mellitus, and HTN medication.
P < .05 was considered statistically significant. Multivariate logistic regression was used to obtain values.
Discussion
Hypertension is a chronic medical condition that results from the interplay between genetic and environmental factors. Africans tend to have higher risk of hypertension than other races.17 -20 The environmental factors which contribute to etiology are also modifiable factors and can include lifestyle factors such as alcohol consumption.20 -23
The current analysis therefore sought to determine the association between alcohol consumption and hypertension among HIV sero-negative and positive cohorts, and the findings support existing literature that, alcohol consumption contributes to the risk of hypertension regardless of one’s HIV status. A number of studies have shown cardiovascular benefits of alcohol consumption.24 -28 Our study was intended to explore this among PLWH.
Our analysis suggested that HIV sero-positive participants tend to consume significant amount of alcohol. This finding is supported by previous studies.28 -30 This suggests that alcohol consumption is indeed a lifestyle among HIV sero-positive individuals, and there is a potential of ART treatment default among these individuals. 20 Alcohol consumption among such individuals may put them at risk of hypertension as shown by our analysis.
This analysis also showed that a significant percentage of the participants; 458 (12.89%) were undiagnosed hypertensives, and among the total of 3553 hypertensive participants, 442 (46.14%) had uncontrolled HTN, while 516 (14.52%) of the hypertensives were not on medication. This finding corroborates existing literature, that undiagnosed hypertension is a real challenge that requires more attention by healthcare providers and all other stake holders.31 -38 This analysis has also shown that PLWH tend to consume significant amount of alcohol, which does not only affect their compliance of ART, but also puts them at risk of developing hypertension. Undiagnosed hypertension is a major risk factor for cardio-metabolic diseases such as stroke and heart attack. The prevalence of stroke and CAD is high in the SSA39 -41 and undiagnosed hypertension contributes significantly to this complications of hypertension. Since hypertension usually does not present with symptoms, people who are undiagnosed are more likely to present with complications of hypertension.
The prevalence of uncontrolled hypertension as shown by our analysis is in line with the findings of other studies.37.38 The high prevalence corroborates with existing literature37,38 that uncontrolled hypertension is a medical problem that requires an urgent attention and intervention by healthcare providers. Several reasons account for the problem of uncontrolled hypertension in SSA.42 -49 These include inadequate access to healthcare facilities and lack of affordable antihypertensive medications in situations where patients are supposed to purchase the medications themselves to treat an asymptomatic condition such as hypertension.42 -49. In fact, the Vukuzazi study was done in a district which was one of the most deprived in South Africa. The distance participants traveled to access healthcare might have contributed to this high prevalence rate of uncontrolled hypertension of 46.14 %. Again, relative to this study, the possible explanation is the inability of health care providers to monitor the effectiveness of their interventions and readjust their intervention specific to each person. The likely reasons that could explain providers’ inability to monitor their interventions can be linked to the high patient-to-practitioner ratio. In fact, SSA challenged with limited human capital. Also, among factors that could explain the growing phenomena of uncontrolled hypertension is non-adherence to treatment regimen because of the side effects of antihypertensive medication; and the perception that all antihypertensives cause impotence in men.
Another finding from this analysis worth noting is the significant association between alcohol consumption and hypertension. Although the finding of this study is similar to the findings of a number of studies which show a linear relationship between alcohol consumption and hypertension,9,18,42 -56; the association between alcohol consumption and hypertension is diverse and multifactorial in pathology ranging from the effect of alcohol on the cardiac muscles where it impairs the mitochondria function resulting in cardio myocytes death thereby risking the consumer to cardiomyopathies. 47 Alcohol consumption among PLWH is associated with increased monocyte activation suggesting possible inflammation.
Conclusion
Alcohol consumption is significantly related to increase BP regardless of HIV infection status. Our findings is a wake-up call for health care providers and other stake holders to do more screening of the modifiable risk factors of non-communicable diseases among PLWH, since such people are at increased risk of cardiometabolic diseases. We therefore recommend that further studies focusing on the effect of alcohol and other behavioral risk factors for cardiometabolic diseases amongst PLWH should be done.
Limitations
Our study could not be short of limitations. We were unable to determine the actual units of alcohol consumed by the participants since it was a self-reported alcohol consumption. Another limitation to this analysis is the fact that alcohol consumption among the participants was obtained by self-reporting. Self-reporting of alcohol use is wroth with challenges of underreporting. Again, the current analysis did not have data on antihypertensive the participants were using at the time of the data collection, and participants’ adherence which may better explain some observations found in this secondary analysis.
Acknowledgments
We acknowledge that the data received from AHRI enabled us to perform this secondary analysis. We are very grateful to the Vukuzazi team members for their technical assistance in interpreting some of the variables used in the dataset.
Footnotes
Authors Contributions: All authors made a significant contribution to this study, whether that is in conception, data analysis, and interpretation. All authors also took part in the drafting, revising, and gave approval for the publication of this manuscript.
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.
ORCID iDs: Manasseh B. Wireko 
https://orcid.org/0000-0002-6454-6964
Isaac K. Owusu
https://orcid.org/0000-0002-8934-8542
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