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Prevalence of hypertension and associated socio-demographic and lifestyle factors among the professional bus drivers in West Bengal, India: A case-control study
*Corresponding author: Dr. Amitava Pal, Ph.D., Department of Physiology, City College, 102/1, Raja Rammohan Sarani, Kolkata, India. amitavaergo@gmail.com
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Received: ,
Accepted: ,
How to cite this article: Pal A, Dalui R, Manna S, Dua P. Prevalence of hypertension and associated socio-demographic and lifestyle factors among the professional bus drivers in West Bengal, India: A case-control study. Ann Natl Acad Med Sci (India). doi: 10.25259/ANAMS_89_2024
Abstract
Objectives
Professional drivers spend a significant portion of their time on congested highways, exposing them to occupational hazards such as air pollution, vehicle exhaust, loud noise, and long working hours, all of which can contribute to health issues. Introduction to these stressors is concomitant with an augmented threat of cardiovascular disorders, making bus drivers particularly susceptible to hypertension. This study aimed to assess the incidence of hypertension and identify associated potential socio-demographic and lifestyle predictors among the professional bus drivers.
Material and Methods
The study involved 267 experienced bus drivers from different districts in West Bengal, India. Participants completed a semi-structured questionnaire regarding their lifestyles and socio-economic backgrounds. Anthropometric measurements such as waist circumference (WC), hip circumference (HC), waist-to-hip ratio (WHR), body mass index (BMI), and blood pressure were documented.
Results
Hypertension was prevalent in 52.06% of the drivers. The risk of hypertension remained elevated among bus drivers, being 1.69 times higher compared to controls. Our research identified a robust association between aging and hypertension; drivers over 50 years old had a six-fold increased risk. Adiposity was strongly linked with hypertension. Overweight/obese drivers had three times higher risk of developing hypertension, with centrally obese drivers having a 2.52 times higher risk. Additionally, factors such as duration of driving, alcohol consumption, and cigarette smoking were independently associated with elevated blood pressure.
Conclusion
The present study highlights a higher incidence of hypertension among professional bus drivers. Consequently, there is a critical need for driver education programs, regular cardiovascular risk factor screenings, and effective interventions.
Keywords
Aging
Alcohol intake
Bus drivers
Hypertension
Job-related factors
Obesity
Risk factors
Smoking
INTRODUCTION
Hypertension is the most important contributor to worldwide early mortality and morbidity.1 Hypertension was the leading contributor of deaths globally from 1990.2 Hypertension rose to become the top risk factor for mortality in 2017, up from fourth place in 1990, and was responsible for more than half of stroke (57.1%) and ischemic heart disease (55.7%) deaths.2 The number of people living with high blood pressure has doubled from 1990 (260 million) to 2019 (640 million) in the Western Pacific and South-East Asia regions. On the contrary, there has been a more than 40% increase in the number of hypertensive people in America and European regions in the last 3 decades.3 Hypertension accounted for 12.8% of all deaths globally.4 High blood pressure presents a significant public health challenge, placing substantial burdens on cardiovascular health and the healthcare system in India.1,5-11 A 2015 national survey spanning 24 Indian states and union territories reported an overall hypertension prevalence of 30.7%.7 In 2016 alone, 1.63 million deaths were reported due to elevated blood pressure in India.8 The NFHS-5 survey (National Family Health Survey Fifth Series) documented 22.8% prevalence of elevated blood pressure in adults. In contrast, in NFHS-4, 20.4% hypertension prevalence was reported.9 A recent ICMR-INDIAB study on 31 Indian states and union territories revealed that 35.5% individuals had high blood pressure, and it was estimated that around 315 million individuals in the country have high blood pressure.10,11 Consequently, early hypertension detection and effective management strategies are crucial public health objectives to mitigate disease burden and associated complications. Population-specific research is essential for informing healthcare policies.
Professional drivers spend most of their time on congested highways, exposed to occupational hazards such as air pollution, automotive exhaust, loud noise, and extended working hours.12-14 Their lifestyles often do not support good health practices. To alleviate mental stresses such as anxiety, depression, and others, they are very much addicted to alcohol and cigarettes, which can lead to health problems. Furthermore, prolonged sitting and reduced physical activity due to their work contribute to higher obesity rates among drivers, exacerbated by consuming high-calorie, low-nutrition foods in restaurants.12-14 Numerous possible factors might be related to hypertension of the professional drivers, these consist of their lifestyle influences like alcohol drinking, smoking, physical activity, etc., socio-demographic features such as age, earnings, sleep duration, genealogy of hypertension, and the presence of coexisting conditions such as diabetes and obesity.15-19 These factors, compounded by stressors such as long work hours, irregular shift patterns, rigid schedules, inadequate cabin conditions, traffic congestion, and passenger-related stressors, increase their susceptibility to developing hypertension. Therefore, this study aimed to assess the incidence of hypertension in professional bus drivers and to identify potential socio-demographic and lifestyle features associated with high blood pressure.
MATERIAL AND METHODS
Study design and subjects
This case-control survey (between February and July 2019) included 267 adult male bus drivers from different districts (Paschim Midnapore, Purba Midnapore, and Howrah) of West Bengal, India. A control group consisting of 104 males with no occupational driving exposure was also included, matched to the bus drivers by age, residency, and socio-economic status. The average ages of the bus drivers and controls were 42.92 ± 10.26 years and 42.56 ± 10.8 years, respectively, showing minimal variation. Bus drivers had an average height and weight of 164.52 ± 6.3 cm and 66.25 ± 11.03 kg, while controls measured 164.54 ± 6.43 cm and 62.74 ± 9.83 kg, respectively. Bus drivers reported an average tenure of 18.64 ± 9.36 years in their profession, working an average of 13.19 ± 2.74 h/day for 6-7 days a week. To ensure understanding and cooperation of the bus drivers and controls, the aim and research procedures were explained verbally to them before the beginning of data collection. Informed agreement to the study of the participants was obtained. The human ethical approval and clearance were taken from the ethics committee of the institution, and this study adheres strictly to the ethical standards and Helsinki Declaration.
Variables
A pre-tested semi-structured questionnaire was developed following review of relevant literature to capture their socio-demographic features like age, income, education, and family size; lifestyle characteristics and work history such as job description, working conditions, daily working hours, preventive measures used, smoking habits, and alcohol intake. The questionnaire underwent a pre-test on ten bus drivers prior to its use in data collection, and these were not included in the present study. Body weight, height, hip circumference (HC), and waist circumference (WC) were taken from the participants by using standard techniques and protocols. Based on World Health Organization (WHO) recommended cut-off values for body mass index (BMI),20 waist-hip ratio (WHR), and waist circumference (WC),21 participants were categorized into various nutritional groups. Resting blood pressure was assessed after a minimum of 15 minutes sitting using a mercury sphygmomanometer and stethoscope, with the mean of three readings recorded for each participant.1 Standard evidence-based guidelines were followed for the classification of blood pressure.22,23
Statistical analysis
Group difference was asses by χ2 test and Student’s t-test for categorical and continuous variables, respectively. The risk of hypertension among bus drivers was estimated using odds ratios (OR), calculated through both bivariate and multivariate logistic regression. SPSS software (Version 20) was used for statistical analyses.
RESULTS
Table 1 illustrates the physical traits of the bus drivers and control participants. Bus drivers exhibited a significantly higher average BMI (p=0.003) compared to the control group, although the BMI of both groups fell within the normal BMI range. Bus drivers also exhibited significantly higher average blood pressure levels than the controls (p≤0.001). A substantial proportion of participants (68.91%) had completed only primary school education and earned than 10,000 INR per month (46.44%). Around 51% participants had families with more than four members. Alcohol and nicotine dependence were notable amongst bus drivers, with approximately 40% reporting alcohol use and 43.45% reporting smoking habits. Moreover, around 42% of bus drivers were overweight or obese, with over half of them categorized as centrally obese. Based on WHR, 32.21% of drivers were classified as moderate risk and 35.21% as high risk.
| Parameters |
Bus driver (n=267) Mean±SD |
Control (n= 104)Mean±SD | t ( p) |
|---|---|---|---|
| Age (years) | 42.92±10.26 | 42.56±10.8 | 0.299 (0.765) |
| Height (cm) | 164.52±6.3 | 164.54±6.43 | 0.027 (0.9782) |
| Weight (kg) | 66.25±11.03 | 62.74±9.83 | 2.836 (0.005) |
| BMI (kg/m2) | 24.44±3.68 | 23.18±3.42 | 3.02 (0.003) |
| Waist circumference (cm) | 88.92±11.05 | 87.47±9.83 | 1.169 (0.243) |
| Hip circumference (cm) | 92.01±5.98 | 91.44±5.72 | 0.835 (0.404) |
| Waist-hip ratio (WHR) | 0.96±0.08 | 0.95±0.07 | 1.119 (0.264) |
| Systolic blood pressure (mmHg) | 139.39±16.98 | 129.4±15.95 | 5.176 (0.001) |
| Diastolic blood pressure (mmHg) | 88.16±10.76 | 83.92±10.85 | 3.401 (0.001) |
| Mean arterial pressure (mmHg) | 105.24±11.87 | 99.08±10.89 | 4.592 (0.001) |
| Duration of employment (Year) | 18.64±9.36 | 19.06±10.27 | 0.378 (0.706) |
| Work per day (h) | 13.19±2.74 | 8.49±2.58 | 15.08 (0.001) |
| Work in a week (day) | 6.61±0.9 | 6.08±0.73 | 5.357 (0.001) |
Statistical significance was set at p < 0.05, BMI: Body mass index; SD: Standard deviation
Hypertension was significantly (p<0.001) prevalent (52.06%) among the bus drivers compared to control participants (31.73%). In the univariate analysis, bus drivers had a significantly higher likelihood of hypertension (COR: 2.34; 95% CI: 1.45-3.77) than controls. Even after adjusting for several covariates, including age, obesity indices, smoking habits, alcohol intake, and socio-economic variables in the multivariate analysis, the risk of hypertension remained elevated among bus drivers, being 1.69 times higher compared to controls [Table 2].
| n | f (%) | χ2 (p) | COR (95% CI) | p | AOR# (95% CI) | p |
|---|---|---|---|---|---|---|
| Control | 33 (31.73) | 12.698 (0.001) | 1 | |||
| Bus Drivers | 139 (52.06) | 2.34 (1.45-3.77) | 0.001 | 1.69 (0.98-2.97) | 0.043 |
Statistical significance was set at p < 0.05, COR: Crude odds ratio, AOR: Adjusted odds ratio, CI: Confidence interval.
#Adjusted for age, obesity indices (BMI, WC, WHR), smoking habit, alcohol consumption, and socio-economic variables
Table 3 explored the association among various variables and hypertension in professional bus drivers. The risk of hypertension was significantly associated with the age of the drivers. Bus drivers aged 50 and above had a 2.8 times greater risk of hypertension than those aged under 30, in the univariate model. After adjusting for multiple co-variables in the multivariate model, bus drivers over 50 years old exhibited a significantly elevated risk, with a 5.85 times higher odds of hypertension. Additionally, being overweight or obese was strongly associated with hypertension; the likelihood of being hypertensive was 2.96 times higher among overweight or obese drivers. Central obesity was also a significant issue of elevated blood pressure (AOR: 2.52; 95% CI: 1.12-4.35). Furthermore, the duration of driving experience was linked to an increased risk of hypertension among participants. According to the univariate logistic regression analysis, drivers with 20-29 years and over 30 years of driving experience had 3.28 times and 4.93 times higher odds of hypertension, respectively, when contrasted with individuals having less than ten years of experience. In the multivariate analysis, 3.61 times and 6.39 times increased odds of elevated blood pressure were observed in bus drivers with 20-29 years and over 30 years of driving experience, respectively. Participants who drove for more than 12 h daily were 5 times more likely to develop hypertension than those driving less than 8 h. Regarding weekly workdays, bus drivers operating every day of the week had more than 5 times the elevated risk of hypertension (AOR: 5.44; 95% CI: 2.18-13.58). Alcohol addiction was also associated with a substantially increased risk of hypertension (AOR: 2.62; 95% CI: 1.19-5.75). Similarly, smokers also had an increased risk of elevated blood pressure (AOR: 2.07; 95% CI: 1.03-4.19) in both univariate and multivariate analyses. Interestingly, socio-economic factors such as schooling level, monthly salary, and family size of the participants did not show significant associations with hypertension in either the univariate or multivariate analyses.
| Variables | Categories | n | f (%) | χ2 (p) | COR (95% CI) | p | AOR (95% CI) | p |
|---|---|---|---|---|---|---|---|---|
| Age | <30 years | 22 | 8 (36.36) |
8.574 (0.036) |
1 | |||
| 30-39 years | 86 | 37 (43.02) | 1.32 (0.5-3.48) | 0.572 | 1.75 (0.43-7.04) | 0.433 | ||
| 40-49 years | 81 | 46 (56.79) | 2.3 (0.87-6.09) | 0.094 | 1.59 (0.33-7.64) | 0.559 | ||
| ≥50 years | 78 | 48 (61.54) | 2.8 (1.05-7.47) | 0.04 | 5.85 (1.18-28.97) | 0.031 | ||
| BMI | Normal | 155 | 70 (45.16) |
7.092 (0.008) |
1 | |||
| Overweight/obese | 112 | 69 (61.61) | 1.95 (1.19-3.2) | 0.008 | 2.96 (1.4-6.22) | 0.004 | ||
| WC | <90 cm | 131 | 57 (43.51) |
7.565 (0.006) |
1 | |||
| ≥90 cm | 136 | 82 (60.29) | 1.97 (1.21-3.21) | 0.006 | 2.52 (1.12-4.35) | 0.017 | ||
| WHR | >0.95 | 87 | 39 (44.83) |
6.862 (0.032) |
1 | |||
| 0.95-0.99 | 86 | 41 (47.67) | 1.12 (0.62-2.04) | 0.707 | 0.85 (0.31-2.29) | 0.75 | ||
| >1 | 94 | 59 (62.77) | 2.07 (1.14-3.76) | 0.016 | 1.07 (0.35-3.22) | 0.91 | ||
| Year of work | <10 years | 42 | 14 (33.33) |
19.412 (0.001) |
1 | |||
| 10-19 years | 107 | 47 (43.93) | 1.57 (0.74-3.3) | 0.239 | 1.63 (0.58-4.63) | 0.355 | ||
| 20-29 years | 66 | 41 (62.12) | 3.28 (1.46-7.39) | 0.004 | 3.61 (1.07-12.15) | 0.038 | ||
| ≥30 years | 52 | 37 (71.15) | 4.93 (2.05-11.9) | 0.001 | 6.39 (1.64-24.9) | 0.008 | ||
| Work per day | Up to 8 hr | 25 | 10 (32) |
7.269 (0.026) |
1 | |||
| 9-12 hr | 106 | 63(48.11) | 1.97 (0.78-4.96) | 0.15 | 1.2 (0.32-4.47) | 0.782 | ||
| >12 hr | 136 | 66 (58.82) | 3.04 (1.23-7.52) | 0.016 | 5 (1.42-17.59) | 0.012 | ||
| Work per week | <7 day | 53 | 21 (39.62) |
4.112 (0.043) |
1 | |||
| 7 day | 214 | 118 (55.14) | 1.87 (1.01-3.46) | 0.045 | 5.44 (2.18-13.58) | 0.001 | ||
| Smoking | Smoker | 116 | 74 (63.79) |
11.42 (0.001) |
2.33 (1.42-3.83) | 0.001 | 2.07 (1.03-4.19) | 0.042 |
| Non-Smoker | 151 | 65 (43.05) | 1 | |||||
| Alcohol consumption | Yes | 106 | 66 (62.26) |
7.389 (0.007) |
1.99 (1.21-3.28) | 0.007 | 2.62 (1.19-5.75) | 0.016 |
| No | 161 | 73 (45.34) | 1 | |||||
| Education | Up to Primary | 184 | 93 (50.54) |
0.546 (0.46) |
0.82 (0.49-1.38) | 0.46 | 0.69 (0.36-1.36) | 0.29 |
| Secondary or above | 83 | 46 (55.42) | 1 | |||||
| Income (Rs) | <10000 | 124 | 59 (47.58) |
1.863 (0.172) |
1 | |||
| ≥10000 | 143 | 80 (55.94) | 1.4 (0.86-2.27) | 0.173 | 1.68 (0.85-3.32) | 0.136 | ||
| Family size | 4 | 131 | 63 (48.09) |
1.624 (0.202) |
1 | |||
| >4 | 136 | 76 (55.88) | 1.37 (0.85-2.21) | 0.203 | 1.21 (0.35-4.55) | 0.752 |
Statistical significance was set at p < 0.05, COR: Crude odds ratio, AOR: Adjusted odds ratio, BMI: Body mass index, WC: Waist circumference, HC: Hip circumference, WHR: Waist-hip ratio, CI: Confidence interval.
DISCUSSION
In this study, hypertension was prevalent in 52.06% of professional bus drivers, whereas control populations exhibited an incidence of around 32%, which was in agreement with findings of the ICMR-INDIAB study11 and Ramakrishnan et al.,7 (2019), community-based national-level survey in India, indicating nearly one in three individuals with hypertension. The incidence of elevated risk of hypertension was 1.69 times greater among professional bus drivers compared to controls in this study. Several studies have corroborated higher hypertension prevalence among professional drivers both in India and internationally.12,16,24-28 For example, Siu et al.24 (2012) documented a hypertension prevalence of 57% among professional drivers in Hong Kong, while Wang and Lin25 observed 56% among bus drivers in Taiwan. Similarly, Shin et al.12 (2013) noted a 53.3% prevalence among bus drivers in a Korean city. In Ghana, Anto et al.27 (2020) conveyed a prevalence of hypertension in drivers was 38.7%, whereas in North Kerala, Lakshman et al.28 (2014) discovered a 41.3% prevalence among the professional drivers. A recent study in Coimbatore, Tamil Nadu, by Devarasu et al.16 (2024) found that around half of the professional bus drivers were suffering from elevated blood pressure. While exact figures vary across studies, these consistent findings underscore hypertension as a significant public health issue among professional drivers worldwide.
Hypertension can arise from various interconnected factors, often hierarchically related. In our study, aging was strongly linked with hypertension, which was in accordance with prior research.16,17,26-29 Professional bus drivers aged over 50 exhibited a six-fold higher risk of hypertension compared to those under 30. This finding aligns with Anto et al.’s (2020) discovery that drivers aged 50-59 faced a 5.43 times greater hypertension risk.27 Aging is a well-recognized independent predictor of cardiovascular diseases, particularly hypertension, as physiological changes affect multiple organ systems, including the cardiovascular system.29,30
Overweight/obese drivers had a 3 times higher risk of developing hypertension, with centrally obese drivers having a 2.52 times higher risk. Other studies have reported comparable results.16,18,19,27,29,31 Fokam et al.19 (2022) stated that the obese drivers had a 4.04 times higher risk of hypertension than non-obese drivers. According to the WHO,32 overweight/obese individuals face two to six times greater risk of hypertension, underscoring the established link between obesity and cardiovascular diseases like elevated blood pressure and coronary heart disease. Mechanisms include renin-angiotensin system involvement, sympathetic nervous system activation, insulin resistance, and sodium retention, all contributing to arterial stiffness and hypertension development.1,29
Additionally, prolonged driving duration was independently associated with hypertension, corroborated by studies by Fokam et al.,19 (2022), Adedokun et al.,29 (2017), Borle & Jadhao,14 and Amadi et al.,33 (2018) all reporting comparable conclusions. Addiction to alcohol and cigarettes was associated with a substantially increased risk of hypertension in our study, consistent with previous research conducted in Ghana27 and South Africa.29 Although alcohol consumption at a moderate level has been linked to potential cardiovascular benefits, particularly in younger bus drivers, heavy drinking is known to adversely affect the cardiovascular system, especially in relation to hypertension.29,34,35 Alcohol can elevate blood pressure through various mechanisms, including stimulation of cortisol secretion, renin-angiotensin-aldosterone system-mediated increased levels of angiotensin II, and disruption of sympatho-adrenal function.36
Study limitation
Several potential factors linked with elevated blood pressure were studied in this study, including lifestyle factors like alcohol drinking, smoking, year of driving, and driving per day, socio-demographic factors like age, income, education, family size, as well as their obesity indicators. Certain lifestyle risk factor, such as physical activity, dietary consumption, and stress, which may contribute to hypertension, was not studied in the present study. Additionally, drug abuse, which can affect blood pressure, was also not studied.
CONCLUSION
The present study highlights a higher incidence of hypertension among professional bus drivers. Aging, obesity, prolonged driving periods, alcohol intake, and cigarette smoking emerged as significant factors associated with hypertension. Therefore, there is a critical need for implementing educational programs and regular cardiovascular risk screenings among professional drivers. These initiatives are essential for early detection of hypertension and the introduction of appropriate interventions to mitigate its impact on driver health and safety.
Acknowledgements
All authors express their sincere gratitude to the study participants for their invaluable contributions to the completion of this study.
Authors’ contributions
AP: Conception and designing, statistical analysis and interpretation, manuscripts preparation, editing and review; RD: Data collection, literature search, manuscript editing and review; SM, PD: Data collection assistance, literature search, manuscript editing and review.
Ethical approval
The research/study approved by the Institutional Review Board at Panskura Banamali College (Autonomous), number IEC/01/19, dated 16th February 2019.
Declaration of patient consent
The authors certify that they have obtained all appropriate participants consent.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
Use of artificial intelligence (AI)-assisted technology for manuscript preparation
The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing or editing of the manuscript and no images were manipulated using AI.
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