Traffic congestion and blood pressure elevation: A comparative cross‐sectional study in Lebanon

Abstract

This comparative cross‐sectional study examines the association between traffic congestion and elevation of systolic and/or diastolic blood pressure levels among a convenience sample of 310 drivers. Data collection took place during a gas station pause at a fixed time of day. Higher average systolic (142 vs 123 mm Hg) and diastolic (87 vs 78 mm Hg) blood pressures were detected among drivers exposed to traffic congestion compared with those who were not exposed (P<.001), while controlling for body mass index, age, sex, pack‐year smoking, driving hours per week, and occupational driving. Moreover, among persons exposed to traffic congestion, longer exposure time was associated with higher systolic and diastolic blood pressures. Further studies are needed to better understand the mechanisms of the significant association between elevated blood pressure and traffic congestion.

1. INTRODUCTION

Hypertension, one of the most prevalent chronic diseases worldwide,1 has been recognized as the leading risk factor for mortality.2 It is defined as a systolic blood pressure (SBP) ≥140 mm Hg or a diastolic blood pressure (DBP) ≥90 mm Hg.3 A wide comparative analysis of 844 studies from 154 countries published between 1980 and 2015 showed an estimated global burden of 874 million adults with hypertension.4 The prevalence of hypertension ranged from 28% in North America to 44% in the European population,5 and varied between Middle Eastern countries, reaching 26.6% in Iran, 31.8% in Turkey, and 27.6% in Palestine.6, 7, 8 The average prevalence of hypertension in the Middle East is predicted to double by the year 2025.9 In Lebanon, the prevalence of hypertension among individuals older than 30 years was 23.1% in 2005.10 Moreover, a cohort study conducted in Beirut between 1983 and 1993 showed that among individuals older than 50 years, the rate of hypertension was 25%.11 A more recent study showed that the prevalence of hypertension among Beirut residents aged 50 years and older increased over the years, reaching 28% in 2005.10

Hypertension constitutes a great public health concern with huge health and economic burdens.1 Evidence shows that it is highly associated with a variety of cardiovascular, cerebral, and renal diseases.12, 13, 14, 15 In addition, the average cost of living with hypertension, including clinical consultations, tests, hospital admissions, medications, devices, adaptive aids, and loss of working days, can reach up to US $3900 per person annually, as is the case in Canada.16

Many risk factors may contribute to the development of hypertension such as obesity, aging, high sodium intake, and mental stress.17, 18, 19 Chronic stressful situations that cause repeated blood pressure (BP) elevations present a major risk factor for the development of hypertension.20 Studies show that the exposure to peak traffic conditions, for example, is associated with elevations in urinary catecholamines,21 an indication of stress, thereby suggesting a link between traffic exposure, stress, and BP elevation. This warrants further investigation.

Lebanon is characterized by its extreme centralization, poor infrastructure, and inadequate public transportation system, which contribute to the increase in traffic congestion.22 In our study, we hypothesize that traffic congestion in Lebanon may be a main contributing factor to the daily transient elevations in BP, thus manifesting itself as a major risk factor for the development of hypertension. To our knowledge, this is the first study to investigate a mass‐scale increase in BP among drivers during the direct stress of real‐life traffic congestion.

2. METHODS

2.1. Study population and procedure

Our study included a convenience sample of consecutive drivers who parked at a gas station to refuel. Drivers were of both male and female sex, any nationality, of ages between 18 and 64 years, and driving on either a traffic‐congested road or a light‐traffic road. We defined traffic‐congested roads as those where people were driving at walking speed with intermittent stopping, and light traffic as those where drivers' speed was not hindered by other drivers. Data were only collected when traffic was congested or light. Drivers who reported being diagnosed with hypertension and/or taking antihypertensive medications, drinking caffeine‐containing beverages (coffee, energy drinks) in the past 30 minutes, engaging in physical activities in the past 30 minutes, or being pregnant (for female drivers) were no longer eligible for participation. However, BP measurement was proposed to them and conducted upon approval in order to ensure justice and equity among all passing drivers who consented for participation and met the basic inclusion criteria. In addition, pamphlets were distributed to all drivers to increase awareness on hypertension and maximize benefits.

Data collection was performed during the first 2 weeks of January 2015 on the Dora highway in the direction of the capital Beirut. Drivers were approached by the researchers while stopping at a gas station for refuel in the mornings. The exposed group was driving in heavy traffic, which happened to be observed during weekdays, while the nonexposed group was driving in light traffic, which happened to be observed during weekends. The procedure took 2 to 3 minutes per driver. The study was first described to the driver and, after securing oral informed consent, a survey with closed‐ended questions was administered and BP measurements were taken. A pilot study was performed before proceeding with data collection.

For an anticipated size effect of 0.15, a statistical power level of 0.9, and a probability level of 0.05, the calculated sample size was 129 for each the exposed and the nonexposed groups. Furthermore, to account for missing data and for nonresponse, the sample size was inflated by 1.3, resulting in 168 needed participants for each the exposed and nonexposed groups. Data collection lasted for 7 days until sample size saturation. Of the 336 total participants, 26 reported having hypertension and thus data on those persons were excluded from further analysis. The final study included 310 participants.

2.2. Study variables

The main outcomes of this study are systolic and diastolic BPs. Measurements were taken in mm Hg using a calibrated wrist digital sphygmomanometer placed on the driver's left arm at heart level while being seated in his or her car. The same machine model was used by all researchers to eliminate any variation in BP measurements.

The drivers were classified into traffic congestion exposed or nonexposed groups based on: (1) the observation of traffic on the road, and (2) a yes/no question to the drivers asking about their current exposure to traffic congestion to ascertain the researcher's observation. Also, those who reported taking alternative roads to reach the gas station as a way to avoid exposure to heavy traffic were excluded from the study. When exposed to traffic congestion, drivers were further asked to indicate the approximate time of exposure in minutes. In addition, demographic data were collated including age (in years), sex, body mass index (BMI, in kg/m²) based on self‐reported weight and height, pack‐year smoking calculated from the number of reported smoked cigarettes per day (1 pack‐year=20 cigarettes/d×1 year), and the number of driving hours in the past week. Drivers were also asked to indicate whether driving was part of their job, which was labeled as occupational driving, or simply a mean of transportation, and whether they were late or not to their destination.

The vehicle‐related factors included the use of air‐conditioning while driving and having the windows closed or open. The investigators also noted the presence (or not) of other passengers in the vehicle.

2.3. Statistical analysis

Descriptive analysis was conducted using frequencies and percentages for categorical variables and means and standard deviations for continuous variables. Normality testing for the main two outcomes systolic and diastolic BPs revealed significant Shapiro‐Wilk tests with P<.001, hence nonparametric Mann‐Whitney U test and Spearman's Rho correlation were used to determine bivariate associations of the outcome measures with categorical and continuous variables, respectively. A P value cutoff of 0.2 was used to select variables for the multivariable analyses. Standardized β and P values of the multiple linear regression models were reported. Significant associations were considered at an α level of 0.05.

3. RESULTS

The drivers' basic characteristics are summarized for the total sample and by exposure to traffic congestion in Table 1. The majority of the participants were men (75%), nonoccupational drivers (70%), and driving alone (76%). The mean age was 39±12 years and a total average number of driving hours per week of 20±19. The exposed and nonexposed groups were comparable in terms of number and basic characteristics, with no significant differences detected.

Table 1.

Bivariate analysis comparing drivers' characteristics by exposure to traffic congestion–Lebanon 2015

Drivers' characteristics		Total N=310	Traffic congestion		P value
			Nonexposed n=151	Exposed n=159
Sex, No. (%)	Male	232 (75)	108 (47)	124 (53)	.190
	Female	78 (25)	43 (55)	35 (45)
Occupational driving, No. (%)	No	218 (70)	109 (50)	109 (50)	.484
	Yes	92 (30)	42 (46)	50 (54)
Passengers on board, No. (%)	No	234 (76)	114 (49)	120 (51)	.996
	Yes	76 (24)	37 (49)	39 (51)
Age, mean (SD), y		39 (12)	39 (12)	39 (13)	.888
Body mass index, mean (SD), kg/m2		26 (5)	26 (5)	26 (4)	.831
Pack‐y smoking, mean (SD)		8 (18)	8 (18)	9 (19)	.469
Driving, mean (SD), h/wk		20 (19)	21 (20)	20 (18)	.770

_{Nonparametric testing was conducted using chi‐square test for categorical covariates and Mann‐Whitney U test for continuous covariates. Significance level was set at an α of 0.05.}

Higher average systolic and diastolic BPs were detected among persons exposed to traffic congestion, with P<.001 (Table 2). Other characteristics significantly associated with increased BPs at the bivariate level were higher BMI, pack‐year smoking, higher driving hours per week, male sex, and reporting occupational driving. Increased age was positively associated with higher level of systolic and diastolic BPs, with significant and borderline P values of .019 and .058, respectively. No significant variability was detected in mean BPs for the status of being late or for any of the vehicle‐related factors.

Table 2.

Bivariate analyses of systolic and diastolic blood pressures with traffic congestion and other covariates using Manm‐Whitney U test and Spearman's Rho correlation–Lebanon 2015

Main exposure		Systolic blood pressure		Diastolic blood pressure
		Mean (SD) or Rho correlation	P value	Mean (SD) or Rho correlation	P value
Traffic congestion	Nonexposed	123 (14)	<.001a	78 (10)	<.001a
	Exposed	142 (18)		87 (13)
Drivers' characteristics
Age, y		.133	.019a	.108	.058
Body mass index, kg/m2		.335	<.001a	.369	<.001a
Pack‐y smoking		.139	.015a	.183	.001a
Driving, h/wk		.214	<.001a	.190	.001a
Sex	Male	137 (17)	<.001a	85 (12)	<.001a
	Female	121 (15)		75 (10)
Occupational driving	No	131 (18)	.001a	80 (12)	<.001a
	Yes	138 (19)		87 (13)
Being late	No	133 (19)	.684	82 (13)	.337
	Yes	133 (17)		83 (12)
Vehicle‐related factors
Passengers on board	No	133 (19)	.797	82 (12)	.312
	Yes	134 (18)		84 (13)
Air‐conditioning	Off	133 (19)	.750	82 (13)	.145
	On	132 (15)		85 (12)
Windows	Closed	133 (17)	.885	82 (12)	.423
	Open	134 (23)		82 (15)

^a _{Significant at P<.05.}

Two multivariable linear regressions were conducted to test for the independent association of exposure to traffic congestion with increased levels of systolic and diastolic BPs. The results in Table 3 show that after controlling for age, sex, BMI, pack‐year smoking, driving hours per week, and occupational driving, there was a significant positive association between being exposed to traffic congestion and an increase in SBP, with a β coefficient of 0.494. Similarly, and controlling for the same covariates in addition to the use of air‐conditioning, exposure to traffic congestion was significantly independently associated with higher DBP (β=0.363). Other factors that showed significant associations with higher BPs were male sex and increased BMI (P<.05). Interestingly, having the air‐conditioning (heater) turned on while driving was significantly associated with increased DBP (β=0.119, P=.014).

Table 3.

Multivariable linear regression analyses for the association between traffic congestion and systolic and diastolic blood pressures controlling for other covariates–Lebanon 2015

	Systolic blood pressure		Diastolic blood pressure
Main exposure	β	P value	β	P value
Traffic congestion (exposed vs nonexposed)	.494	<.001a	.363	<.001a
Covariates
Age, y	.059	.204	−.008	.873
Sex (female vs male)	−.237	<.001a	−.163	.004a
Body mass index, kg/m2	.170	.001a	.187	.001a
Pack‐y smoking	.063	.181	.097	.063
Driving, h/wk	.114	.052	.030	.650
Occupational driving (yes vs no)	−.037	.531	.096	.145
Air‐conditioning (on vs off)			.119	.014a

^a _{Significant at P<.05.}

Further analyses were conducted to examine the correlation between duration of exposure to traffic congestion and increased BPs among the subsample of 159 participants. At the bivariate level, a longer exposure time was significantly associated with higher systolic and diastolic BPs (Rho correlations of 0.196 and 0.228, respectively; P<.05). Other factors found to be positively associated with a rise in SBP were older age, higher BMI, higher driving hours per week, and male sex. On the other hand, increased DBP was associated with higher BMI, male sex, and occupational driving compared with their respective counterparts (data not shown). At the multivariable level, the correlations between traffic congestion exposure time and systolic and diastolic BPs remained in the positive direction (β=0.140 and 0.137, respectively) but lost its significance at the 0.05 level (Table 4). Sex was a more significant predictor for higher systolic and diastolic BPs than other factors in both models, with higher risk for men than women.

Table 4.

Multivariable linear regression analyses for the association between systolic and diastolic blood pressures and traffic congestion exposure time controlling for other covariates among those exposed to traffic congestion (n=159)–Lebanon 2015

	Systolic blood pressure		Diastolic blood pressure
Main exposure	β	P value	β	P value
Traffic congestion exposure time, min	.140	.059	.137	.075
Covariates
Age, y	.154	.039a	.034	.658
Sex (female vs male)	−.303	<.001a	−.201	.023a
Body mass index, kg/m2	.064	.453	.109	.227
Driving, h/wk	.087	.242
Occupational driving (yes vs no)			.140	.079

^a _{Significant at P<.05.}

4. DISCUSSION

This is the first comparative cross‐sectional study, to our knowledge, that tested the association between exposure to traffic congestion and BP elevation in a real‐life setting with a large sample size. Exposure to traffic congestion was found to be significantly associated with an increase of both systolic and diastolic BPs from an average of 123 mm Hg and 78 mm Hg to 142 mm Hg and 87 mm Hg, respectively (Table 2). The increase of SBP by 19 mm Hg and DBP by 9 mm Hg is clinically significant. Repetitive exposure to such a clinically significant stressor may eventually result in the development of hypertension.20

Moreover, it is worth noting that even factors that cause mild elevations in BP without reaching the level of clinical hypertension may directly increase the risk of morbidity. Two thirds of strokes and almost half of the cases of ischemic heart disease are correlated with SBPs >115 mm Hg, a value considered to be within the normal range.1, 13, 23 This further highlights the importance of the significant change in BP documented in our study and portrays mass‐scale traffic congestion as a serious long‐term public health hazard.

A study by Evans and Carrere21 reported the effect of driving during traffic congestion on BP‐elevating mechanisms in the body, thereby establishing a possible mechanistic link between the two. In their study of the public transportation driver population, the effect of the on‐the‐job stress of driving in peak traffic congestion was assessed by measuring urinary catecholamine level–a marker for stress.21 Therefore, these findings may constitute a possible physiologic explanation for the elevation of BP documented in our study.

Previous studies have addressed this relationship in a variety of different indirect methods. In a study by Fairclough and Spiridon24 men were subjected to a driving simulation and exposed to a traffic jam at some point delaying them from reaching their target. The study showed that traffic delays significantly increase BP, heart rate, and total peripheral resistance, among other physiological alterations. However, the authors focused on the concept of being delayed from reaching the destination. In our study, we found no significant correlation between the drivers' reports of being late and an elevation in BP. This might be attributable to the fact that we could not control for or further explore the difference in the drivers' perceptions of the importance of reaching their goal destinations in time and the variability in the consequences of being late.

When looking at the population of drivers exposed to traffic congestion, it is noticeable that older age was associated with an increase in SBP but not DBP. This is in concordance with the current knowledge pertaining to the physiologic changes of the vascular system that occur with aging; whereby the stiffening of the arteries decreases compliance, thus elevating SBP and restricting DBP increase.25

Among drivers exposed to traffic congestion, the duration of exposure was associated with higher SBP with a borderline significant P value (.059) at the multivariable analysis level (Table 4). In other words, the longer the drivers are exposed to traffic congestion, the higher the probability for their SBP to rise. Thus, an improvement of the transportation system that minimizes the time drivers spend in traffic is likely to hinder BP elevation among drivers, protecting them from possible long‐term health effects, namely chronic hypertension.

In our study, “occupational drivers,” who were mostly public transportation drivers, had higher BP measurements compared with their counterparts according to bivariate analysis (P<.05) (Table 2), regardless of their exposure status to traffic congestion. This association is congruent with our hypothesis, which suggests that frequent recurrent transient elevations in BP can lead to higher baseline BP and probably hypertension.20 However, it did not stand at the level of the multivariable linear regression (P=.145) (Table 3). This might be explained by the fact that the overall elevation in BP among occupational drivers can be influenced by other confounding variables than driving for long hours and being exposed to traffic congestion that we could not control for such as socioeconomic status, sedentary lifestyle, and diminished access to routine health care.

The investigation of possible factors, such as having passengers on board and closing the windows, that were hypothesized to help reduce this increase in BP, did not show any statistically significant association. Meanwhile, turning on the heating system was associated with an elevated DBP in the total sample but not in the traffic congestion group, which might be the result of a reduction in the sample size. In light of that, further studies might be able to bring a better understanding of the contributory role of behavioral interventions in influencing BP. Since many of the hypothesized contributory factors for BP changes were not found to have statistically significant associations, more efforts need to be made to address the traffic congestion itself.

5. STUDY LIMITATIONS

This study is not without some limitations. First, BP measurements were performed at the wrist level for feasibility purposes instead of using a BP cuff on the arm, which would have led to a higher accuracy in measurement. Second, self‐reported items in the questionnaire, such as weight, height, driving hours in the past week, and frequency of cigarette smoking, are subject to a recall bias. Third, our study was performed at one location, which could limit its generalizability to the driving population commuting in this area only. Fourth, patients with undiagnosed hypertension might have been unknowingly included in our sample, which might overestimate the effect of our variable of interest on BP levels. Nevertheless, Table 1 indicates that both groups of our sample were well balanced, which might have controlled for the effect of undiagnosed hypertension in both exposure groups. Last, there could potentially be an inherent difference in BP readings on weekends as opposed to weekdays whereby drivers might have had lower readings during weekends, a potential confounder to traffic congestion exposure.

6. CONCLUSIONS

The correlation between driving in traffic congestion and BP elevation should push decision‐makers to further invest in the development of public transportation in Lebanon. Interestingly enough, more than 77% of the drivers encountered in this study were solo drivers, shedding light on the huge deficiency of the Lebanese public transport system and lack of culture to use it among Lebanese citizens. Further studies should be performed to ensure wider generalizability of our results and to determine its reproducibility in populations that deal differently with the stress induced by traffic congestion. Also of interest is the investigation of the role of the driver's destination (work vs other) on the association between traffic congestion and BP elevation. In addition, comparing the BP level of drivers with that of other passengers also stuck in traffic congestion might provide more insight toward this line of research. Finally, long‐term prospective or retrospective cohort studies would allow the ascertainment of traffic congestion as a risk factor for the development of hypertension among the population of drivers.

CONFLICT OF INTEREST

The authors have no conflicts of interest to declare.

7. ETHICAL APPROVAL

All procedures performed in this study were in accordance with the ethical standards of the institutional review board at the American University of Beirut and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

P Bou Samra, P El Tomb, M Hosni, et al. Traffic congestion and blood pressure elevation: A comparative cross‐sectional study in Lebanon. J Clin Hypertens. 2017;19:1366–1371. 10.1111/jch.13102