Introduction
Hypertension (HTN) is well established as the most common chronic disease worldwide. It is predicted that the total number of hypertensive patients will rise from 972 million individuals in 2000 to 1.56 million individuals in 2025 [1]. Since HTN is asymptomatic in acute stages, the morbidity and mortality resulting from it could be dramatically high [2]. Approximately 47% of ischemic heart diseases and 57% of stroke cases, being among the prime causes of death in the world, are related to HTN [3-5]. Besides, about 9.5 million annual deaths (16.5% of the total deaths) are attributed to HTN [6]. It was observed that the incidence and mortality of kidney diseases caused by HTN in 2007 and 2017 significantly increased by 28% and 41.4%, respectively [5, 7]. Hence, identification of high-risk subjects and risk factors affecting HTN prevalence is a high priority.
Professional driving puts drivers under stressful conditions, such as a sedentary lifestyle, an unhealthy diet plan, irregular work schedules [8-10], external stressors, such as noise [11], vibration [12], air pollution [13, 14], and toxic vehicle exhaust emissions [15]. Exposure to these factors turns professional drivers, including bus, truck, and taxi drivers, into high-risk groups for cardiovascular diseases (CVDs). In the past few decades, some studies investigated CVDs among professional drivers. The results of a cohort study, which investigated hospital admissions among professional drivers in Denmark, indicated that age-standardized hospital admissions ratios (SHRs) for CVDs, including ischemic heart disease, myocardial infarction, and cerebrovascular disease, were significantly higher in male taxi and bus drivers than in other drivers [16]. Another study indicated a stronger risk of myocardial infarction among drivers [17]. Furthermore, in another study, a higher risk of stroke was reported in drivers than in the general population [18]. Similarly, in a series of other studies which investigated cardiovascular risk factors, greater risks of metabolic syndrome [19-22], poor sleep quantity and quality [23-26], as well as HTN [27-36] were reported among professional drivers.
Up until now, although many studies have investigated the prevalence of HTN among professional drivers, including bus and truck drivers, little evidence exists on investigating the prevalence of HTN among taxi drivers. In addition, few studies have examined the risk factors influencing the prevalence of HTN among drivers, and in particular, among taxi drivers. To the best of our knowledge, no study has investigated this association among Iranian taxi drivers. Therefore, this study aims to fill this gap by determining the prevalence of HTN and identifying its determinants among Iranian taxi drivers.
Materials and Methods
This cross-sectional study was carried out in Kermanshah province located in western Iran in 2018 in the three months of spring to early summer. Multistage sampling was used to select the study participants. To this end, Kermanshah province was divided into the five central, western, eastern, southern, and northern areas (strata). Next, a similar number of participants were selected from each study area (about 74 drivers in each area). We determined the sample size according to formula 1 as follows:[1]
Where 𝑛 indicates the required sample size at the 95% level of significance, 𝑝 represents the prevalence of HTN among the taxi drivers, which is set at 0.4 according to the previous studies [8, 27, 29-30, 38-41], and 𝑑 is the degree of precision set at 0.05. Accordingly, the sample size consisted of 369 subjects. In the present study, we only included male taxi drivers licensed by the Taxi Organization, so private taxi drivers were not included. After data collection, taxi drivers with less than a one-year work experience were excluded. In the end, the data obtained from 321 taxi drivers were analyzed.
For the purpose of data collection, a two part self-administrated questionnaire, including questions about demographic information and work-related factors, was employed. Demographic data included age, height, weight, marital status (married or single), current smoking habits, education levels (high school diplomas, lower degrees, and university degrees), economic status (poor status was ≤ a 2-million Toman income per month, medium status was between a 2- and a 3-million Toman income per month, and good status was > a 3-million Toman income per month), as well as self-reported diseases and familial medical history. Besides, work-related factors included work experience, work hours per day (WHPD), work days per week (WDPW), self-reported fatigue (SRF), and sleep duration. We used an analog scale for measuring SRF, which ranged from 0 (no fatigue) to 100 (the highest level of fatigue). Sleep duration was determined for each subject by questions "at what hours at night did you usually go to sleep and at what hours did you wake up in the past months?" Moreover, a body mass index (BMI) was calculated for each participant via dividing weight (kg) by height (m2). In addition, the participants' blood pressure was measured by a Beurer BC16 Blood Pressure Monitor according to the standard protocol [37]. In this study, HTN was defined as systolic blood pressure (SBP) ≥140 mmHg or diastolic blood pressure (DBP) ≥ 90 mmHg, according to the latest definition provided by the European Society of Cardiology/the European Society of Hypertension (ESC/ESH) [37]. It is worth noting, to minimize the measurement errors, blood pressure was measured again at a 10-minute interval for each participant, with the average blood pressure computed and recorded in the end.
After importing the data to SPSS software version 25 and classifying them, the normality of the distribution of the quantitative variables was determined by the Shapiro-Wilk test. Accordingly, the Mann–Whitney test was utilized to assess the significance of the difference between the study groups. In addition, to investigate the significant difference between the study groups for nominal variables, the Chi-square test was utilized. In this study, quantitative and nominal variables were represented by means (the standard deviation) and numbers (percentages). In addition, logistic regression was utilized to examine the effects of study variables on the risk of HTN. Besides, logistic regression was presented by the odds ratio (OR) at a 95% confidence interval (CI) for both unadjusted and adjusted models. It is worth noting that the Spearman’s correlation coefficient was employed to examine correlations between the study variables before including them in the model. Moreover, in the adjusted logistic regression model, those variables were considered, which were significantly different among the study groups, or which had a significant association with HTN (p-value < 0.10) in the simple logistic regression.
Results
According to the study results, 59.8% of the participants (192/321) suffered from HTN. The summary of the study groups' characteristics, including demographic data, work-related factors, as well as self and family medical histories have been provided in Table 1. The results indicate a significant difference between the two groups only in the variables of age, work history, WDPW, marital status, education levels, and self-reported economic status (p<0.05).
Table 1. The summary of the characteristics of the included taxi drivers, Kermanshah province, 2018
Variables |
Total subjects
(n=321) |
Study groups |
P-value |
NHTN
(n=129) |
HTN
(n=192) |
Mean (SD)/
N (%) |
Mean (SD)/
N (%) |
Mean (SD)/
N (%) |
SBP, mmHg |
137.3 (21.52) |
120.1 (10.8) |
148.9 (19.0) |
- |
DBP, mmHg |
86.0 (11.7) |
78.3 (7.1) |
91.2 (11.3) |
- |
Age, (year) |
43.8 (12.1) |
40.7 (10.9) |
45.9 (12.4) |
< 0.001a |
BMI, (kg/m2) |
25.1 (3.0) |
25.2 (2.9) |
25.0 (3.1) |
0.452a |
Work history, (year) |
7.3 (8.3) |
4.6 (4.8) |
9.1 (9.6) |
< 0.001a |
WHPD |
8.6 (2.1) |
8.2 (1.7) |
8.8 (2.3) |
0.051a |
WDPW |
5.9 (1.0) |
6.0 (1.0) |
5.8 (1.1) |
0.030 a |
SRF, (%) |
69 (20.0) |
70.0 (20.6) |
68.2 (19.7) |
0.583 a |
Sleep duration, (hour) |
7.0 (1.3) |
7.2 (1.3) |
6.9 (1.2) |
0.088 a |
Current smoking |
Yes |
128 (39.9) |
54 (41.9) |
74 (38.5) |
0.552 b |
Marital status |
Married |
272 (84.7) |
99 (76.7) |
173 (90.1) |
0.001b |
Regular exercise |
Yes |
70 (21.8) |
33 (25.6) |
37 (19.3) |
0.179 b |
Education |
University degree |
54 (16.8) |
32 (24.8) |
22 (11.5) |
0.002b |
High school diploma and less |
267 (83.2) |
97 (75.2) |
170 (88.5) |
Economic status, income in a month |
Poor, (≤ 2 million Tomans) |
148 (46.1) |
47 (36.4) |
101 (52.6) |
0.004 b |
Medium and good (> 2 million Tomans) |
173 (53.9) |
82 (63.6) |
91 (47.4) |
Previous diagnosed diseases |
HTN, yes |
88 (27.4) |
29 (22.5) |
59 (30.7) |
0.104 |
Kidney, yes |
67 (20.9) |
29 (22.5) |
38 (19.8) |
0.561 b |
Diabetes, yes |
72 (22.4) |
27 (20.9) |
45 (23.4) |
0.598 b |
Heart, yes |
57 (17.8) |
21 (16.3) |
36 (18.8) |
0.570 b |
Family disease history |
HTN, yes |
131(40.8) |
46 (35.7) |
85 (44.3) |
0.124 b |
Kidney, yes |
112 (34.9) |
45 (34.9) |
67 (34.9) |
0.998 b |
Diabetes, yes |
97(30.2) |
39 (30.2) |
58 (30.2) |
0.996 b |
Heart, yes |
102 (31.8) |
43 (33.3) |
59 (30.7) |
0.623 b |
Abbreviations: HTN: hypertension; NHTN: non-hypertensive; SD: standard deviation; N, number; BMI: body mass index; WHPD: work hours per day; WDPW: work days per week; SRF: self-reported fatigue; SBP: systolic blood pressure; DBP: diastolic blood pressure
a Mann–Whitney test for the difference between the two groups; b Chi-square test for the difference between the two groups;
A difference is significant at p-value < 0.05.
The results of the logistic regression are presented in Table 2. In both adjusted and unadjusted models, it was observed that the subjects aged ≥ 45 years, married, and with poor economic status had a significantly higher risk of HTN than their reference group. In addition, in terms of the variable of educational level, there was a significantly higher risk of HTN in the subjects with a high school diploma and less, than the reference group in the unadjusted model at p-value <0.05 and in the adjusted model at p-value < 0.10.
Table 2. The logistic regression models for hypertension in the included taxi drivers according to demographic and medical history data, Kermanshah province, 2018
Variables |
No (%)
HTN/NHTN |
Logistic regression models |
Simple |
Multiple |
OR (95%CI) |
p-value |
OR (95%CI) |
p-value |
Age a |
< 45 |
97(50.5)/82(63.6) |
Referent |
- |
Referent |
- |
≥ 45 |
95(49.5)/47(36.4) |
1.71(1.08,2.70) |
0.022 |
1.68(1.04,2.76) |
0.035 |
BMI b |
< 25 |
59(41.3)/70(39.3) |
Referent |
- |
Referent |
- |
≥ 25 |
84(58.7)/108(60.7) |
0.92(0.59,1.44) |
0.726 |
0.90(0.56,1.46) |
0.672 |
Marital status a |
Single |
19(38.8)/30(61.2) |
Referent |
- |
Referent |
- |
Married |
173(63.3)/99(36.4) |
2.76(1.48,5.16) |
0.001 |
2.57(1.32,5.01) |
0.006 |
Regular exercise b |
Yes |
37(52.9)/33(47.1) |
Referent |
- |
Referent |
- |
No |
155(61.8)/96(38.2) |
1.44(0.84,2.46) |
0.181 |
1.21(0.68,2.15) |
0.517 |
Smoking habit b |
No |
118(61.1)/75(38.9) |
Referent |
- |
Referent |
- |
Yes |
74(57.8)/54(42.2) |
0.87(0.55,1.37) |
0.552 |
0.91(0.56,1.47) |
0.685 |
Education level c |
University |
22(11.5)/32(24.8) |
Referent |
- |
Referent |
- |
High school diploma and less |
170(88.5)/97(75.2) |
2.55(1.40,4.63) |
0.002 |
1.73(0.92,3.28) |
0.092 |
Economic status b |
Good |
91(47.4)/82(63.6) |
Referent |
- |
Referent |
- |
Poor |
101(52.6)/47(36.4) |
1.94(1.23,3.06) |
0.005 |
1.72(1.07,2.78) |
0.026 |
Previous diagnosed disease b |
|
|
|
|
|
|
HTN |
No |
133(69.3)/100(77.5) |
Referent |
- |
Referent |
- |
Yes |
59(30.7)/29(22.5) |
1.53(0.91,2.56) |
0.105 |
1.35(0.77,2.37) |
0.293 |
Kidney |
No |
154(60.6)/100(39.4) |
Referent |
- |
Referent |
- |
Yes |
38(56.7)/29(43.3) |
0.85(0.49,1.47) |
0.561 |
0.78(0.44,1.39) |
0.395 |
Diabetes |
No |
147(59.0)/102(41.0) |
Referent |
- |
Referent |
- |
Yes |
45(62.5)/27(37.5) |
1.16(0.67,1.98) |
0.598 |
0.90(0.50,1.61) |
0.713 |
Heart |
No |
156(59.1)/108(40.9) |
Referent |
- |
Referent |
- |
Yes |
36(63.2)/21(36.8) |
1.19(0.66,2.14) |
0.570 |
1.18(0.62,2.24) |
0.610 |
Family medical history b |
|
|
|
|
|
|
HTN |
No |
107(56.3)/83(43.7) |
Referent |
- |
Referent |
- |
Yes |
85(64.9)/46(35.1) |
1.43(0.91,2.67) |
0.124 |
1.19(0.72,1.94) |
0.499 |
Kidney |
No |
125(59.8)/84(40.2) |
Referent |
- |
Referent |
- |
Yes |
67(59.8)/45(40.2) |
1.00(0.63,1.60) |
0.998 |
0.83(0.50,1.38) |
0.471 |
Diabetes |
No |
134(59.8)/90(40.2) |
Referent |
- |
Referent |
- |
Yes |
58(59.8)/39(40.2) |
1.00(0.61,1.62) |
0.996 |
0.88(0.52,1.48) |
0.627 |
Heart |
No |
133(60.7)/86(39.3) |
Referent |
- |
Referent |
- |
Yes |
59(57.8)/43(42.2) |
0.89(0.55,1.43) |
0.623 |
0.75(0.45,1.25) |
0.264 |
Abbreviations: HTN: hypertension; NHTN: non-hypertensive; OR: odds ratio; CI: confidence interval; BMI: body mass index
a WHPD, WDPW, sleep duration, and economic status
b WHPD, WDPW, sleep duration, economic status, and age
C WHPD, WDPW, sleep duration, economic status, and work history
An association is significant at p-value < 0.05.
The effects of work-related factors, including work history, WHPD, WDPW, SRF, and sleep duration on the risk of HTN have been presented in Table 3. We observed that drivers with a 5-year experience and higher who worked 10 hours and more per day had a significantly higher risk of HTN than the reference group. Interestingly, there was a lower risk of HTN in the subjects who worked 7 days per week than those who worked 6 days and less per week.
Table 3. The logistic regression models for hypertension in the included taxi drivers according to work-related factors, Kermanshah province, 2018
Variables |
No (%)
HTN vs. NHTN |
Logistic regression models |
Simple |
Multiple |
OR (95% CI) |
P-value |
OR (95% CI) |
P-value |
Work history a |
< 5 |
84(43.8)/89(69.0) |
Referent |
- |
Referent |
- |
≥ 5 |
108(56.3)/40(31.0) |
2.86(1.79,4.58) |
< 0.001 |
2.20(1.34,3.63) |
0.002 |
WHPD b |
≤8 |
97(50.5)/75(58.1) |
Referent |
- |
Referent |
- |
9-10 |
65(33.9)/47(36.4) |
1.08(0.66,1.73) |
0.785 |
1.08(0.65,1.81) |
0.763 |
≥10 |
30(15.6)/7(5.4) |
3.31(1.38,7.96) |
0.007 |
2.64(1.02,6.81) |
0.045 |
WDPW c |
≤ 6 |
134(69.8)/77(59.7) |
Referent |
- |
Referent |
- |
7 |
58(30.2)/52(40.3) |
0.64(0.40,1.23) |
0.062 |
0.55(0.33,0.90) |
0.018 |
SRF d |
< 60 |
78(40.6)/58(45.0) |
Referent |
- |
Referent |
- |
≥ 60 |
114(59.4)/71(55.0) |
1.19(0.76,1.87) |
0.441 |
1.23(0.76,1.99) |
0.390 |
Sleep duration d |
≥ 7 |
69(35.9)/39(30.2) |
Referent |
- |
Referent |
- |
≤ 6 |
123(64.1)/90(69.8) |
1.30(0.80,2.09) |
0.289 |
1.43(0.86,2.38) |
0.167 |
Abbreviations: HTN: hypertension; NHTN: non-hypertensive; OR: odds ratio; CI: confidence interval; BMI: body mass index; WHPD: work hours per day; WDPW: work days per week; SRF: self-reported fatigue; HTN: hypertension; NBP: normal blood pressure
a WHPD, WDPW, sleep duration, economic status, and education levels
b WDPW, sleep duration, economic status, and age
c WHPD, sleep duration, economic status, and age
d WHPD, WDPW, sleep duration, economic status, and age
An association is significant at p-value < 0.05.
Table 4 shows the unadjusted and adjusted logistic regression models for the estimation of the risk of HTN in the case of per unit increase in the quantitative variables. In both models, upon a one-year increase in the work history, the risk of HTN increased significantly. However, it was observed that upon a one-day increase in WDPW, the risk of HTN decreased significantly in both models. According to the unadjusted model, there was a significantly higher risk of HTN in terms of the variables of WHPD and sleep duration. In contrast, in accordance with the adjusted model, the risk was significant at p<0.10. In terms of other variables, including BMI and SRF, no significant association was observed.
Table 4. The logistic regression models for hypertension per 1-fold increase in the quantitative variables among taxi drivers, Kermanshah province, 2018
Variables |
Logistic regression models |
Simple |
Multiple |
OR (95% CI) |
p-value |
OR (95% CI) |
p-value |
Age a |
1.04(1.02,1.06) |
< 0.001 |
1.04(1.02,1.06) |
0.001 |
BMI b |
0.99(0.91,1.06) |
0.696 |
0.97(0.90,1.05) |
0.512 |
Work history c |
1.10(1.05,1.14) |
< 0.001 |
1.08(1.03,1.13) |
< 0.001 |
WHPD d |
1.14(1.02,1.27) |
0.023 |
1.11(0.98,1.26) |
0.095 |
WDPW e |
0.77(0.62,0.97) |
0.024 |
0.73(0.57,0.94) |
0.013 |
Sleep duration b |
0.85(0.71,1.1) |
0.069 |
0.83(0.69,0.99) |
0.050 |
SRF b |
1.00(0.99,1.01) |
0.599 |
1.00(0.99,1.01) |
0.779 |
Abbreviations: OR: odds ratio; CI: confidence interval; BMI: body mass index; WHPD: work
hours per day, WDPW: work days per week; SRF: self-reported fatigue.
a WHPD, WDPW, sleep duration, and economic status
b WHPD, WDPW, sleep duration, economic status, and age
c WHPD, WDPW, sleep duration, economic status, and education levels
d WDPW, sleep duration, economic status, and age
e WHPD, sleep duration, economic status, and age
An association is significant at p-value < 0.05.
Discussion
One of the objectives of this study was to determine the prevalence of HTN among Iranian taxi drivers, which had not been investigated in the past. It was observed that the prevalence of HTN was significantly high among Iranian taxi drivers. Accordingly, more than half of the participants suffered from HTN (59.8%). In this study, a higher prevalence of HTN was observed than in previous published studies that reported the prevalence of HTN within the range of 18.2% and 57% [8, 27, 29-30, 38-41]. Furthermore, the comparison of the results of the present study with other studies that investigated the prevalence of HTN among other professional drivers, including suburban, bus, and truck drivers, showed that the prevalence of HTN was significantly higher in taxi drivers than in other professional drivers [28, 32-33, 42-46]. There are several reasons for the higher prevalence of HTN among taxi drivers, among which one could refer to simultaneous exposure to several risk factors, such as a sedentary lifestyle, an unhealthy diet plan, irregular work schedules [8-10], as well as external stressors, such as noise [11], vibration [12], air pollution [13, 14], and toxic vehicle exhaust emissions [15].
Another purpose of this study was to determine the factors probably playing a significant role in the prevalence of HTN among taxi drivers. To this end, we collected data from different lifestyle, medical, and work-related factors and used them in the multiple logistic regression models to examine the association between these factors and HTN. According to the results, a one-year increase in the participants' age significantly increased the risk of HTN by 4%. According to the categorical analysis, subjects aged ≥ 45 had a significantly higher risk of 68% in HTN than subjects aged < 45. These findings were similar to those of most previous studies on professional drivers [30, 33-35, 41, 43]. However, some studies show no significant association between the drivers' age and the prevalence of HTN [36, 38]. Nevertheless, aging is a well-established risk factor for HTN. Aging, through several biological pathways, including inflammation, oxidative stress, and endothelial dysfunction, could lead to an increased risk of HTN [47]. In this study, a significant association was established between work history and HTN. Accordingly, there was a higher risk of HTN by %8, per one year increase in work history. Besides, the risk of HTN increased significantly by 120% in the subjects with work history ≥ 5 years versus work history < 5 years. This finding was in line with previous studies that reported an increased risk of HTN with a rise in work history [27, 30, 34, 41, 43]. It is worth noting that due to the high correlation between age and work history, the variable of age played an interfering role in the association.
In addition, we investigated several work-related risk factors, including WDPW, WHPD, SRF, and sleep duration. According to the findings from this research, taxi drivers working more than 10 hours per day were 2.64 times more at the risk of HTN than the reference group (workers who worked 8 hours and less per day). In addition, there was a significantly higher risk of HTN upon an hour increase in WHPD (at p-value <0.10), which was due to long sedentary times and less physical activities among the drivers. It is well established that low levels of physical activity or long sedentary times could significantly raise the risk of HTN. Moreover, taxi drivers working more hours per day could be more exposed to other parameters associated with HTN. According to some studies, exposure to traffic noise and air pollution could significantly raise the risk of HTN as well. In addition, drivers working more than 10 hours per day reported shorter sleep durations per night than drivers working 9-10 hours per day (6.4 vs.7.2 h) as well as drivers who worked eight hours and less per day (6.4 vs. 7.1 h). Short sleep durations could be associated with a higher risk of HTN. In addition, a meta-analysis reported a significant dose-response relationship between sleep duration and HTN [26].
Interestingly, we noticed a lower risk of HTN (45%) in the drivers working all days per week than the workers who had at least one day off. In contrast, upon a one-day increase in the working days, the risk of HTN decreased by 27%. Although the two groups had no significant differences in age, BMI, SRF, and sleep duration, the drivers working all days per week had averagely 2 years of lower work history than the other group. In this study, the participants' sleep durations were examined as well, according to which, there was no significant association between the study groups in terms of sleep duration. In addition, it was observed that upon a one-hour increase in sleep duration, the risk of HTN significantly declined by 17%. A previous meta-analysis reported a significant dose-response relationship between sleep duration and HTN [26]. However, no significant association was observed between SRF status and prevalence of HTN among taxi drivers in this study.
In terms of lifestyle factors, a significant association was observed among marital status, economic status (p-value< 0.05), and education levels (p-value <0.10). In addition, married taxi drivers were shown to be 2.57 times more than others at the risk of HTN, which was consistent with some previous studies [30, 33]. Accordingly, this finding could be due to the higher mean age (46.4 vs. 29.5), longer work history (8.1 vs. 2.7), and higher BMI (25.2 vs. 24.3) in the married drivers than in the single ones. However, some studies did not show a significant association between marital status and HTN [38, 43]. In terms of the variable of economic status, there was a higher risk of HTN by 72% in the drivers who reported poor economic status than in the reference group. This finding was consistent with a study that investigated this relationship among bus drivers in India [43]. However, it was inconsistent with other studies that showed no significant difference between hypertensive and non-hypertensive drivers in terms of economic status [30, 33, 34]. It was also observed, in this study, that drivers with lower educational levels (high school diplomas and less) had a significantly higher risk of HTN (by 73%) than subjects with university diplomas, at the significance level of p<0.10. This finding was consistent with a study conducted on 491 taxi drivers in Brazil [41]. Although some studies did not show a significant association between educational levels and HTN [33, 34, 43], a recent meta-analysis reported that lower educational levels, as an independent risk factor, increased the risk of HTN (pooled OR= 2.02 and 95% CI= 1.55–2.63) [49]. In the present study, no significant association was found between the variables of BMI, smoking habit, physical activity, medical history, and HTN. These findings were similar to the previous studies on BMI [36], smoking habits [30, 33-36, 50], physical activity [33, 38, 50], medical history and HTN [36], diabetes [33, 36], family HTN [33], and family diabetes [33].
This study was faced with several limitations. Therefore, the results should be interpreted cautiously. Firstly, a cross-sectional design has some limitations in showing cause-effect relationships. Secondly, the use of a convenience sampling method with 321 drivers is not representative of the entire taxi driver population. Thirdly, we were not able to investigate the participants' diet plans. Finally, some of the variables in the present study, including sleep duration and SRF, were collected by subjective methods that could have been associated with measurement errors.
Conclusion
This study was the first attempt to determine the prevalence of HTN among Iranian taxi drivers. In addition, it was a unique attempt to identify risk factors playing a significant role in the prevalence of HTN in the taxi drivers. The findings obtained consider taxi drivers as a high-risk group in terms of HTN. Besides, it was demonstrated that the variables of age, work history, WHPD, WDPW, sleep duration, economic status, and education level could be significantly associated with the prevalence of HTN among taxi drivers. More studies are required, especially longitudinal ones, to confirm the present study findings and to overcome the mentioned limitations.
Acknowledgement
The authors of the present study would like to appreciate the participants involved in this study.
Conflict of interest: None declared.