Prevalence of Noise-Induced Hearing Loss among Truck Drivers: a Cross-Sectional Study in Lucknow

Abstract

Background:

Noise pollution is a significant occupational hazard for heavy-vehicle drivers. This study aimed to determine the prevalence of hearing loss, specifically noise-induced hearing loss, among truck drivers.

Methods:

A total of 200 long-distance truck drivers based in Lucknow City were selected for this study. Pure tone audiometry was used to measure air conduction thresholds in each ear at frequencies ranging from 250 to 8000 Hz. The association of risk factors, such as sleep duration, work experience, age, alcohol drinking, and diabetes, with the prevalence of hearing loss among truck drivers was examined. Participants completed a structured questionnaire addressing hearing health and noise exposure. Data were analyzed using Python software, employing statistical tests such as the chi-square test, rank-sum test, and Wilcoxon signed-rank test.

Results:

Based on the World Health Organization guideline, the overall prevalence of hearing loss among 200 drivers was 50.5% in their better ear, with a higher hearing loss rate in the right ear (73.5%) than in the left ear (59.5%). The average hearing thresholds at 500, 1000, 2000, and 4000 Hz of the drivers’ right and left ears were 32.61 ± 9.85 and 28.66 ± 8.88 dB, respectively. The audiometric analysis identified key risk factors for hearing loss, including shorter sleep duration (≤6 h), extended work experience (>10 years), older age (>40 years), and alcohol drinking, which were further confirmed as significant predictors through multivariate logistic analysis.

Conclusion:

Hearing loss is highly prevalent among truck drivers, with the right ear being more affected. The study underscores the need for regular hearing assessments and protective measures to mitigate hearing impairment risks.

KEY MESSAGES:

• (1)Noise-induced hearing loss is highly prevalent among truck drivers due to prolonged exposure to high noise levels.
• (2)Elevated hearing thresholds among drivers, especially at high frequencies, indicate early signs of noise-induced hearing loss.
• (3)Extended driving hours, combined with factors like age and alcohol consumption, are associated with a high risk of hearing loss in this group.
• (4)Short sleep duration is linked to increased hearing loss, highlighting the effect of lifestyle factors on auditory health.

INTRODUCTION

Noise pollution, as an omnipresent urban risk, permeates every facet of modern life, affecting physical and mental well-being. It can cause discomfort, stress, sleep disturbances, hormonal changes, an increase in blood pressure, heart attack, and reduced quality of life.[1] Noise-induced hearing loss (NIHL) occurs when cochlear hair cells are damaged, leading to gradual impairment and eventual deafness.[2] Due to its painless progression, NIHL is often overlooked but poses a considerable occupational hazard, particularly for those working in noisy environments, such as heavy-vehicle drivers. NIHL typically begins at frequencies greater than normal conversation, making early detection challenging. Consequently, individuals may experience prolonged noise exposure without noticing hearing impairment until identified through audiometric testing. Among heavy-vehicle drivers, truck drivers navigating city streets and highways face substantial exposure to noise pollution. These drivers, essential to logistics, are at risk of auditory health issues due to prolonged vehicular and urban noise exposure.[3,4,5]

The World Health Organization (WHO) identifies prolonged exposure to noise levels exceeding 85 dB as detrimental to hearing health, potentially leading to hearing impairment in one or both ears. In line with this, global research has extensively studied occupational noise exposure across various industries, reflecting the widespread concern about its effect on workers’ health and productivity. For instance, Zein-Elabedein et al.[6] reported an association between NIHL and genotoxicity among textile workers in Egypt, suggesting that occupational noise can cause auditory and cellular damage. Similarly, a study in Myanmar found that 66.4% of textile workers were exposed to harmful noise levels, with 25.7% experiencing hearing loss, highlighting the high risk of NIHL in noisy work environments.[7] Research among welders in Mysuru City revealed significant associations between noise exposure duration and health effects, with 11.5% reporting hearing loss. Despite moderate awareness (53.7%) of noise hazards, limited use of ear protection highlighted the need for improved safety practices.[8] A recent study among steelworkers reinforced these findings, reporting that 71% of workers suffered hearing impairment and 47% experienced NIHL, with younger workers and those with tinnitus showing higher hearing thresholds.[9]

These studies collectively demonstrated the pervasive risk of NIHL across various industries. However, a notable gap in research focusing on truck drivers remains, particularly in India. Existing literature predominantly focuses on auto-rickshaw drivers and noncommercial drivers, often overlooking the unique noise exposure patterns encountered by truck drivers.[10,11,12] While some studies address the hearing health of professional bus drivers, research on truck drivers remains limited.[13,14] This gap is critical given their prolonged exposure to high noise levels, extended driving hours, poor road conditions, and lack of access to hearing protection.

Lucknow, the capital city of Uttar Pradesh with a population of 4.12 million, exemplifies this issue. Many truck drivers based here regularly transport goods through noisy environments, posing severe auditory risks. Investigating the causes and incidence of hearing loss among this group is vital for implementing preventive measures to protect their auditory health.

MATERIALS AND METHODS

Participants

This study used a cross-sectional approach to evaluate the prevalence of hearing loss, specifically NIHL, among truck drivers. A total of 200 truck drivers were selected from various transportation hubs and depots in Lucknow, ensuring representation across different age groups and years of work experience. The truck drivers were selected from the two busiest routes: (1) Lucknow to Faizabad and (2) Lucknow to Kanpur. These locations were chosen on the basis of their high traffic of long-distance trucks and strategic importance in the region’s transportation network. Drivers were approached during their rest periods to minimize disruption to their work schedule. The selection criteria included drivers who had been employed as long-distance truck drivers for at least 1 year and who regularly operated on these routes. Due to the general mindset in India that heavy vehicles are predominantly driven by males, and based on the authors’ visits to various transportation hubs, only male truck drivers were available and included in the study. Drivers with a history of ear surgeries or diagnosed hearing impairments unrelated to noise exposure were excluded to focus the study on NIHL.

Sample Size Calculation

The required sample size for the study was calculated to be 165 to achieve a statistically significant result with an estimated prevalence of NIHL of 30%, a confidence level of 95%, and a precision (margin of error) of 7%.[15] However, a sample size of 200 was taken to enhance statistical power, ensure adequate representation across different subgroups (e.g., age and work experience), and reduce the impact of potential dropouts.

Data Collection and Audiometry

Data collection was conducted from September 2022 to May 2023. Trained practitioners measured all pure-tone air-conduction hearing thresholds in a soundproof room by using a digital diagnostic audiometer (Classic Model, Prime 9, New Delhi) with supra-aural headphones (Telephonics TDH-39P, Arphi Electronics Pvt. Ltd., Mumbai). The hearing tests were conducted in the morning hours before the drivers began their driving shifts to enhance accuracy. Pure-tone hearing thresholds were assessed for both ears at frequencies of 250, 500, 1000, 2000, 3000, 4000, and 8000 Hz, with intensity levels ranging from 0 dB to 110 dB.[16] On the basis of the WHO guideline, the average hearing thresholds at 500, 1000, 2000, and 4000 Hz greater than 25 dB in the better ear was diagnosed as NIHL.[17] The participants completed a structured questionnaire, which consisted of six sections covering sociodemographic details, work patterns, substance use, perceived stress, sleep quality, and self-reported hearing difficulties. It includes 50 questions, presented as binary options (Yes/No) or multilevel scales (3–5 levels) for assessing the behavior and symptoms. While these self-reported data were subject to recall bias, the study primarily relied on objective audiometric assessments, which helped mitigate the potential impact of this bias on the findings.

Data Analysis

The collected data were analyzed using Python (version 3.12.3, Wilmington, DE, USA), specifically performing data manipulation using “pandas” library, using “NumPy” for numerical computations, and “StatsModels” and “SciPy” for statistical tests and regression analyses. Hearing thresholds were summarized using medians and interquartile ranges (IQRs) due to their non-normal distribution (p < 0.05), as confirmed by the Shapiro–Wilk test. Wilcoxon signed-rank test was applied for paired comparisons between right and left ear thresholds. The chi-square test was used to assess the difference between two independent categorical variables. Multivariate logistic regression analysis was used to evaluate the correlation between hearing loss prevalence and demographic factors such as age, driving hours, sleep duration, years of work experience, alcohol consumption, and the presence of diabetes. The statistical significance was set as p < 0.05 for all tests.

Ethical Considerations

This study obtained approval from the Institutional Ethics Committee at King George Medical University, UP, Lucknow (letter number: 1973/Ethics/2022, approval number: 113th ECM 11 B-PhD/P2) prior to data collection. Informed consent was obtained from all participants, assuring their voluntary involvement and confidentiality of the data.

RESULTS

Work Experience and Demographic Profile

The study involved 200 male drivers with a mean work experience of 16.64 ± 9.17 years, ranging from 1 to 45 years. The average age of participants was 39.49 ± 10.18 years, with the daily work duration averaging 10.18 ± 2.27 h. Details of sociodemographic information are summarized in Figure 1. This demographic profile indicates a diverse participation pool, which is crucial for understanding the context of the prevalence and impact of NIHL within this group.

Figure 1.

Sociodemographic characteristics of drivers. Note: SC, schedule cast; OBC, other backward cast.

Self-Reported Hearing Difficulties

The self-reported hearing difficulties among the drivers were assessed using a structured questionnaire. While most participants reported minimal difficulties in everyday hearing tasks like phone conversations and group discussions, a notable number struggled with hearing in noisier environments. Specifically, many drivers noted difficulty hearing in background noise and received complaints from others about their tendency to listen to music or watch television at high volumes [Table 1].

Table 1.

Self-Reported Hearing Difficulties Among Drivers.

S. No.	Question	Never	Rarely	Sometimes	Often	Mostly
1	Do you feel any problem in hearing while talking on the phone?	184	11	5	0	0
2	Do you have any trouble following the conversation when talking in a group?	189	0	11	0	0
3	Do you have trouble hearing in a noisy background?	143	23	31	2	1
4	Do you frequently need to ask people to repeat what they say?	167	11	20	2	0
5	Do people complain that you listen to music or watch TV at high volume?	74	26	53	15	32
6	Do you get annoyed because you misunderstand what others say?	98	10	14	40	38

Audiometric Analysis and Hearing Loss Severity

In this study, the overall prevalence of hearing loss among 200 drivers was 50.5% in their better ear, with a higher prevalence of 73.5% in the right ear compared with 59.5% in the left ear. Figure 2 provides a detailed breakdown of the percentage of drivers experiencing hearing loss at each specific frequency. Single-frequency assessments showed a greater impact on the right ear, with an abnormal hearing rate of 84% at 4000 Hz. Additionally, the rank-sum test revealed a statistically significant difference in hearing loss rates between the two ears across frequencies from 250 to 4000 Hz (p < 0.001). However, the hearing loss rates were similar in both ears at 8000 Hz (p > 0.05).

Figure 2.

Hearing loss rates of right and left ears at different frequencies.

The average hearing thresholds at 500, 1000, 2000, and 4000 Hz for the 200 drivers were measured for both ears, showing 28.66 ± 8.88 dB in the left ear and 32.61 ± 9.85 dB in the right ear. The hearing thresholds across all the tested frequencies (250–8000 Hz) are summarized in Table 2 by using medians and IQRs. The analysis of paired thresholds for each driver revealed consistently higher thresholds in the right ear than in the left ear (p < 0.001, Wilcoxon signed-rank test).

Table 2.

Hearing Threshold Levels of Right and Left Ears at Different Frequencies

Frequency (Hz)	Right Ear (dB)		Left Ear (dB)		p*
	Median	IQR	Median	IQR
250	30.0	28.75–35.0	25.0	25.0–30.0	<0.001
500	30.0	25.0–35.0	25.0	20.0–30.0	<0.001
1000	25.0	20.0–35.0	25.0	20.0–30.0	<0.001
2000	30.0	20.0–40.0	25.0	20.0–35.0	<0.001
3000	32.5	25.0–40.0	25.0	25.0–35.0	<0.001
4000	40.0	30.0–45.0	30.0	25.0–45.0	<0.001
8000	40.0	25.0–45.0	30.0	25.0–40.0	<0.001

*Wilcoxon signed-rank test applied.

Factors Associated with Hearing Impairment

This study examined the association of lifestyle and demographic factors, such as sleep duration, age, work experience, alcohol consumption, and diabetes, with hearing health among drivers. The drivers were divided into two groups for each risk factor to assess variations in hearing impairment. The descriptive analysis revealed that the drivers with shorter sleep durations (≤6 h), older age (>40 years), and alcohol consumption demonstrated a higher prevalence of hearing impairment in both ears. The drivers with over 10 years of work experience showed greater impairment in the right ear. By contrast, diabetes showed no significant association with hearing impairment despite a slightly higher prevalence among drivers with diabetes (p > 0.05). The various associations were statistically assessed using chi-square tests, and full details are presented in Table 3.

Table 3.

Distribution of Hearing Impairment across Different Variables

Variable	Group	Total (n)	Right ear				Left ear
			Normal	Impaired	χ 2	p*	Normal	Impaired	χ 2	p*
Sleep duration (h)	≤6	97	17 (17.53%)	80 (82.47%)	6.82	0.008	19 (19.59%)	78 (80.41%)	11.76	<0.001
	>6	103	36 (34.95%)	67 (65.05%)			62 (60.19%)	41 (39.81%)
Work experience (years)	≤10	63	26 (41.27%)	37 (58.73%)	9.34	0.002	30 (47.62%)	33 (52.38%)	1.53	0.217
	>10	137	27 (19.71%)	110 (80.29%)			51 (37.23%)	86 (62.77%)
Age (years)	≤40	113	40 (35.40%)	73 (64.60%)	9.56	0.002	56 (49.56%)	57 (50.44%)	7.97	0.005
	>40	87	13 (14.94%)	74 (85.06%)			25 (28.74%)	62 (71.26%)
Alcohol consumption	Yes	105	20 (19.05%)	85 (80.95%)	5.53	0.019	33 (31.43%)	72 (68.57%)	6.74	0.009
	No	95	33 (34.74%)	62 (65.26%)			48 (50.53%)	47 (49.47%)
Diabetes	Yes	18	2 (11.11%)	16 (88.89%)	1.62	0.204	5 (27.77%)	13 (72.22%)	0.81	0.368
	No	182	51 (28.02%)	131 (71.98%)			76 (41.75%)	106 (58.24%)

*Chi-square test of independence applied.

Table 4 presents the results of multivariate regression analysis to further examine the independent effects of these risk factors and quantify their associations with hearing loss. Due to a correlation between age and work experience, two separate models were used: one that included age, sleep duration, and alcohol consumption as independent variables and another that replaced age with work experience. In both models, hearing loss was the dependent variable for both ears. When age was included, key predictors of hearing loss were sleep duration and age. In the model that accounted for work experience, hearing loss showed a significant association with sleep duration (in both ears) and work experience (in the left ear). Additionally, alcohol consumption showed a consistent association with hearing loss in both ears across both models. This approach allowed for an enhanced understanding of the individual effects of age and work experience on hearing loss.

Table 4.

Multivariate Logistic Regression Analysis for Risk Factors of Hearing Loss

Variable	Right Ear					Left Ear
				95% CI					95% CI
	β	OR	p-Value	Lower	Upper	β	OR	p-Value	Lower	Upper
Regression Model: Excluding Work Experience
Sleep duration	−0.220	0.803	0.001	0.756	0.852	−0.232	0.793	<0.001	0.749	0.839
Age	0.045	1.046	0.020	1.015	1.078	0.050	1.051	0.013	1.010	1.094
Alcohol consumption	0.160	1.174	0.002	1.078	1.278	0.150	1.162	0.001	1.073	1.259
Regression Model: Excluding Age
Sleep duration	−0.225	0.798	0.001	0.748	0.852	−0.236	0.789	<0.001	0.745	0.837
Work experience	0.005	1.094	0.090	1.046	1.145	0.070	1.073	0.030	1.020	1.127
Alcohol consumption	0.170	1.185	0.005	1.089	1.290	0.180	1.197	0.000	1.083	1.323

Note: β = regression coefficient, CI = confidence interval, and OR = odds ratio.

DISCUSSION

The results revealed a clear correlation between noise exposure and hearing loss, with a trend of increasing hearing thresholds and variability at high frequencies. This pattern aligns with the known effects of NIHL, which often have a severe impact on high frequencies. In the past, few studies have focused on noise exposure among heavy-vehicle drivers. A study by Patwardhan et al.[18] on bus drivers in Sangli, exposed to noise levels of 89–106 dB, reported that 89% of drivers had abnormal audiograms, indicating impaired hearing. Another study in Mazandaran Province reported that 37.5% of bus and truck drivers had hearing loss in the right ear and 41.8% in the left ear.[19] In Iran, Pourabdian et al.[3] reported that 26.8% of the drivers experienced hearing loss, with the left ear being more affected than the right ear. Similarly, Bagla et al.[13] found that 36% of bus drivers in North India suffered from hearing loss, with 12% regularly exposed to noise levels exceeding 85 dB.

In this study, the observed asymmetry in hearing loss between ears may be influenced by unique factors specific to the population, such as asymmetrical noise exposure due to driving positions, environmental conditions, or habitual behavior like window preference while driving. Additionally, the findings revealed a strong association between sleep duration and hearing loss. An odds ratio below 1 further confirmed this association, aligning with Clarke et al.’s review.[20] Among recent studies, Awad et al.[21] highlighted correlations between sleep characteristics and tinnitus leading to hearing loss, and Wang et al.[22] investigated the connection between sleep apnea and hearing impairment. Similarly, research in China by Rong et al.[23] revealed that shorter sleep was linked with a higher risk of visual and hearing impairments.

The results underscore the importance of adequate sleep, noise management, and regular hearing monitoring, as hearing loss strongly correlates with age and work experience. This finding is consistent with the findings of Alizadeh et al.,[19] who reported increased hearing loss with advancing age and prolonged exposure to loud noise. Lopes et al.[24] and Majumdar et al.[25] confirmed that longer work experience (>10 years) is linked to higher hearing impairment, particularly at high frequencies (3000 and 4000 Hz) than at lower frequencies.

Apart from variables like age, working hours, and work experience, this study examined the correlation between alcohol consumption, diabetes, and hearing loss. A systematic review by Qian et al.[26] found mixed results on alcohol as a risk factor for hearing loss, with nine of nineteen studies reporting a positive association and ten studies finding no relation. The findings (Table 4) of the present study reinforce the potential auditory risks associated with alcohol, consistent with studies indicating that chronic alcohol use may damage cochlear hair cells, impair central auditory processing, and reduce inner ear blood flow, increasing the risk of ear infections, all contributing to hearing loss.[27,28]

Diabetes poses risks to auditory health by damaging the blood vessels and nerves of the inner ear, with high blood sugar levels causing inflammation and oxidative stress. Diabetic neuropathy can affect the auditory nerve, reducing the brain’s ability to process sound.[29,30] A recent study by Li et al.[31] suggested a potential causal relationship between diabetes and hearing loss, and a cross-sectional study in Eastern India reported a 70.4% prevalence of sensorineural hearing loss among 152 participants with diabetes.[32] In the present study, the audiometric findings indicated that drivers with diabetes may have a higher prevalence of hearing loss than drivers without diabetes; however, this difference was not statistically significant. This finding suggests that while hearing loss is common among individuals with diabetes, diabetes itself may not be a strong independent risk factor for hearing loss in this population.

Limitations and Future Scope

While PTA is a gold standard for identifying hearing impairment, its subjective nature relies on patient cooperation, which may introduce variability and affect accuracy. Additionally, the study’s focus on truck drivers from Lucknow limits the generalizability of the results to other groups or regions with different demographic and occupational characteristics. Future studies could include a broader sample across regions and incorporate objective measures like auditory brainstem response testing to reduce variability and improve accuracy. Sreedhar et al.[33] found that brainstem coding of speech signals may help detect hearing issues early, even in individuals with normal hearing. While research on occupational hearing loss among truck drivers is well documented in countries like Iran, studies specifically targeting Indian truck drivers remain limited, emphasizing the need of more focused research in this demographic.[3,34,35,36]

Intervention Strategies for Hearing Conservation

Implementing hearing protection devices (HPDs) like earplugs and earmuffs to mitigate NIHL risk among truck drivers is essential, chosen to match noise levels and driver comfort. Additionally, occupational health programs should educate drivers on noise risks, proper HPD use, and the importance of regular hearing assessments.[37,38] Combining technical and educational approaches can greatly enhance hearing conservation efforts.

CONCLUSION

The study highlights the hearing threshold levels among truck drivers, revealing significant disparities between the left and right ears, particularly at high frequencies such as 4000 and 8000 Hz. These frequencies exhibited higher thresholds in the right ear, suggesting specific vulnerability at these ranges. Factors, such as age, sleep duration, work experience, and alcohol consumption, were linked to hearing loss, reflecting the cumulative effects of prolonged occupational noise exposure. Early detection through audiometric evaluations and structured questionnaires proved to be effective, even when drivers did not report symptoms. These findings underscore the importance of regular hearing assessments and protective measures to help prevent further hearing impairment in this population.

Availability of Data and Materials

All data generated and analyzed in this study are provided within this published article.

Author Contributions

AM contributed to literature search, data acquisition and statistical analysis of data, drafting of the manuscript, SPS contributed to the study concept and design of intellectual content, MKM contributed to ethical approval, critical revision of the manuscript, intellectual content, and study supervision, SKS contributed to manuscript drafting, AKS contributed to data analysis and overall supervision, MM contributed to manuscript preparation, editing, and review. All authors participated in the interpretation of the results. The guarantor is Anupam Mehrotra.

Ethical approval

This study obtained ethics approval from King George’s Medical University, U.P., Institutional Ethics Committee, Lucknow, prior to data collection. Letter no. 1973/Ethics/2022, Reference Code-113th ECM 11 B- Ph.D /P2, dated 13/01/2022.

Consent to participate

Informed consent was taken from all participants, assuring their voluntary involvement and confidentiality of the data.

Conflicts of interest

The authors declare that there is no conflict of interest.

Funding Statement

Not applicable.