Degree, Pattern, and Risk Factors of Hearing Loss Among Adults of Eastern India

Article information

J Audiol Otol. 2026;30(1):51-59

Publication date (electronic) : 2026 January 20

doi : https://doi.org/10.7874/jao.2025.00290

Srilatha Kavarthapu ¹

, Asuri Raagini ²

, Babban Jee ³

, Deepali Singh ¹

, Ahammad Basha Shaik ⁴

, Bhimraj Balkrishna Ramteke ²

¹Department of Otorhinolaryngology-Head and Neck Surgery, Manipal Tata Medical College, Manipal Academy of Higher Education, Manipal, India

²Department of Otorhinolaryngology-Head and Neck Surgery, Tata Main Hospital, Jamshedpur, East Singhbhum, India

³Department of Research, Manipal Tata Medical College, Manipal Academy of Higher Education, Manipal, India

⁴Department of Community Medicine, Manipal Tata Medical College, Manipal Academy of Higher Education, Manipal, India

Address for correspondence: Srilatha Kavarthapu, MS, Department of Otorhinolaryngology-Head and Neck Surgery, Manipal Tata Medical College, Jamshedpur Manipal Academy of Higher Education, Manipal, India, Tel +91-0657-221-1122, Fax +91-06572211133, E-mail Srilatha.k@manipal.edu

Received 2025 May 7; Revised 2025 August 6; Accepted 2025 September 8.

Abstract

Background and Objectives

Hearing loss (HL) is a significant global public health concern with an increasing burden falling on India because of factors such as aging, noise exposure, and such comorbidities as hypertension and diabetes. Despite these known risks, comprehensive data are lacking on the causes and patterns of HL in East Singhbhum, Jharkhand, especially in industrial settings. This study aims to address this gap by examining the contributing factors to HL among the local population.

Subjects and Methods

This study included 295 adult patients of both sexes, aged 20–80 years, diagnosed with HL. Pure tone audiometry (PTA) was performed to assess the pattern and degree of HL.

Results

Of the 295 cases, most were females (52.54%); however, male participants, especially factory workers (p=0.005), had a higher prevalence of HL (p=0.038). Moreover, younger age (all p<0.05), alcohol use (p=0.003), alcohol and smoking (p<0.001), noise exposure with smoking (p=0.004), hypertension (p= 0.037), diabetes with hypertension (p=0.045), other comorbidities (p=0.018), and unilateral HL (p=0.002) appeared as significant risk factors for HL, as did various clinical diagnoses, including presbycusis (p<0.001), chronic otitis media (p= 0.003), noise-induced HL (p=0.007), sudden sensineural HL (p<0.001), and benign paroxysmal positional vertigo (p=0.001).

Conclusions

Noise exposure combined with a smoking habit, use of alcohol without smoking, smoking and alcohol, hypertension without diabetes, diabetes with hypertension, and other predisposing factors were key contributors to HL, highlighting the need for early intervention and management. The most common diagnosis was chronic otitis media.

Keywords: Hearing loss; Smoking; Noise exposure; Sensorineural hearing loss; Conductive hearing loss; Chronic otitis media

Introduction

Hearing loss (HL) is one of the most common sensory deficits worldwide, affecting millions of individuals and significantly diminishing quality of life. In India alone, approximately 63 million individuals experience substantial auditory impairment. This burden is expected to increase, as the global prevalence of HL is expected to reach 630 million by 2030 [1,2]. The consequences of HL go beyond auditory function, influencing communication, education, employment, mental health, and social integration [3,4]. The burden of HL is especially high among older adults, with more than 25% of individuals over 60 years affected [5], contributing significantly to years lived with disability [6].

Multiple risk factors contribute to hearing impairment, including aging, chronic otitis media, noise exposure, diabetes, hypertension, and lifestyle behaviors such as smoking and alcohol use [7–9]. Sensineural hearing loss (SNHL) is often associated with aging and systemic comorbidities, whereas conductive hearing loss (CHL) is frequently linked to middle ear pathologies, such as chronic suppurative otitis media [10]. Less recognized causes include autism [11], central auditory processing disorders [12], and tumors [13]. Noise remains a significant and preventable cause of HL, especially in occupational settings. It is considered a pervasive industrial pollutant, often leading to noise-induced hearing loss (NIHL), which typically manifests as bilateral, symmetrical, high-frequency SNHL [14–17]. A plethora of studies suggest that genetics and immune response also play a vital role in the causation of HL [18].

Despite the prevalence of HL, particularly in industrial regions of India, epidemiological data exploring its multifactorial etiology remain sparse. This study aims to examine the degree, pattern, and magnitude of risk factors associated with HL among adults attending a tertiary care hospital in East Singhbhum, an industrial district of eastern India. By identifying the relative contribution of various demographic, occupational, lifestyle, and medical factors, this research seeks to support targeted prevention and early intervention strategies.

Subjects and Methods

Study design and ethical clearance

An analytical cross-sectional study was conducted from February 2024 to July 2024 in the ENT Department of Tata Main Hospital (TMH), a tertiary care center attached as a teaching hospital to Manipal Tata Medical College, Jamshedpur, East Singhbhum, India. The study was started after obtaining ethics approval (No. TMH/IEC/MAR/142/2024) from the Institutional Review Board of TMH and Manipal Tata Medical College. Consent to participate was obtained from all participants involved in the study.

Study population and sampling

A hospital-based cross-sectional observational study with analytical components, using a convenience sampling method, was employed, and 295 patients presenting with complaints of HL were included from the Department of ENT. Patients aged 20–80 years presenting with complaints of HL who consented were included in the study. Patients unable to respond to pure tone audiometry (PTA) or who did not provide informed consent were excluded from the study.

Clinical examination and audiometry

Each patient underwent a detailed medical history review and a comprehensive examination of the ear, nose, and throat. The history of hypertension and diabetes mellitus was retrieved from patients’ medical history and further confirmed by clinical records.

An otoscopic examination was first performed, followed by microscopic evaluation of the ear to assist in diagnosis. Hearing assessment was conducted using PTA in a soundproof room meeting ISO 8253–1:2010 standards, with the MAICO MA 53 clinical audiometer (MAICO Diagnostics GmbH) and standard headphones. The audiometer was calibrated according to ANSI S3.6–2018 standards, and examinations were conducted by certified audiologists with 10 years of clinical experience. The TDH39 headphones were used to measure air conduction hearing thresholds, while the B71 headphones were used for bone conduction hearing thresholds for each ear in a two-room soundproof setup. PTA was conducted from 250 Hz to 8 kHz for air conduction and from 250 Hz to 4 kHz for bone conduction thresholds. The Hughson-Westlake procedure was employed to determine the hearing thresholds for each ear. The World Health Organization (WHO) classification criteria (mild: 26–40 dB; moderate: 41–60 dB; severe: 61–80 dB; and profound: >80 dB) for HL were used to classify the clinical cases. HL was further classified as conductive, sensorineural, or mixed [2,19].

Statistical analysis

The demographic data, including age, sex, religion, and location, were recorded for each patient. The collected data were organized and tabulated using Microsoft Excel 2016, and statistical analysis was performed using the Statistical Package for Social Sciences (SPSS) version 27.0 (IBM Corp.) and RStudio Version 2025.05.1+513 (Posit Software, PBC). The data were analyzed using appropriate statistical methods, with the chi-square test applied to examine associations. To determine a significant linear trend in the binomial proportion across ordinal distribution of the variables across increasing levels of HL severity, the Cochran–Armitage Trend test was used. A multiple linear regression analysis was conducted with the average PTA threshold (dB HL) as the dependent (continuous) variable, representing the severity of HL. The estimated effects (β coefficients) with a 95% confidence interval (CI) for HL were calculated. The model included predictors such as age group, sex, occupation, personal history (e.g., smoking, noise exposure), type of HL, and diagnosis, to examine factors associated with the degree of hearing impairment in patients already diagnosed with HL. A p-value less than 0.05 was considered statistically significant.

Results

Sociodemographic characteristics

The demographic characteristics of the study population are shown in Table 1. The study sample (n=295) consisted of slightly more females (52.54%) than males (47.46%). Sex distribution did not show a statistically significant difference (χ²(1)=0.763, p=0.382), suggesting sex parity in HL presentation. On the other hand, the age distribution of participants was statistically significant (χ²(4)=65.288, p<0.001), indicating that HL was not evenly distributed across age groups. The highest proportion of participants was aged between 46 and 60 years (33.22%), followed by those aged 61–75 years (26.44%) and 30–45 years (21.36%). Participants aged above 75 years accounted for 8.14% of the sample. Occupational status also showed a significant association with the distribution of HL (χ²(2)=73.329, p<0.001), with factory workers and non-worker individuals each accounting for 45.08% of the study population.

Table 1

Sociodemographic characteristics of the study population

This substantial representation of factory workers underscores the relevance of occupational noise exposure as a potential risk factor, although a definitive diagnosis of NIHL was limited by the lack of detailed exposure histories. These findings confirm the heterogeneity of the study sample. The statistically significant variation across age and occupational categories justifies further subgroup analysis and supports the inclusion of these variables in regression models for HL prediction. It also indicates that the risk of HL is multifactorial and varies significantly based on demographic and occupational factors, reinforcing the need for stratified public health interventions.

Pattern and causative factors of HL

Table 2 summarizes the pattern of HL among 295 participants based on personal history, predisposing factors, diagnosis, type, severity, and PTA findings. Most participants (82.37%) reported no personal history, while smaller proportions reported smoking (6.44%), alcohol use (3.73%), or both (2.71%), with a significant association between personal history and HL pattern (χ²(7)=1,322.708, p<0.001). Nearly half (47.80%) had no known predisposing factors, while 13.22% had diabetes, 16.95% hypertension, and 20.68% both, also showing a significant association (χ²(4)=173.051, p<0.001). Chronic otitis media (34.20%) and presbycusis (28.10%) were the most common diagnoses, followed by others such as NIHL (7.80%), BPPV (5.10%), acute suppurative otitis media (ASOM) (4.70%), and sudden SNHL (3.70%), with a significant distribution (χ²(6)=190.610 p<0.001). Most participants had bilateral HL (94.92%) versus unilateral (5.08%) (χ²(1)= 238.051, p<0.001), and the majority had moderate (50.51%) or mild (31.86%) loss, with fewer showing severe (11.86%), profound (3.05%), or normal hearing (2.71%) (χ²(4)=254.271, p<0.001). PTA results showed 45.76% had SNHL, 26.10% CHL, 25.42% mixed, and 2.71% normal hearing, with a significant association (χ²(3)=109.65, p<0.001).

Table 2

Pattern of hearing loss in the study population

Association between the level of HL and various variables

The association between the level of HL and various demographic and clinical variables is presented in Table 3. There was a statistically significant association between age group and level of HL (p<0.001). Participants aged 46–60 and 61–75 years exhibited a higher prevalence of moderate to severe HL, while younger age groups (15–30 and 31–45 years) were more commonly in the normal to mild HL categories.

Table 3

Association between the level of hearing loss and various variables

The analysis revealed that the severity of HL was significantly associated with several factors, including age (p<0.001), occupation (p=0.005), personal history (such as smoking and noise exposure) (p<0.001), predisposing medical conditions (diabetes and hypertension) (p<0.001), type of HL (p<0.001), PTA classification (p<0.001), and diagnosis (p<0.001). Older age groups, factory workers, and individuals with combined risk factors (e.g., smoking, noise exposure, or comorbidities) were more likely to have moderate to severe HL. Bilateral HL and sensorineural or mixed types were more common in higher severity categories. Chronic otitis media and presbycusis were the most frequent diagnoses in moderate to severe cases.

The Cochran-Armitage test for trend showed significant linear associations between HL severity and key binary variables. Male participants had a higher grade (severe) of HL (Z=2.08, p=0.038), and bilateral HL became more common with increasing severity (Z=−2.92, p=0.004). Factory workers also showed a significant trend toward more severe HL (Z= 2.84, p=0.005), indicating a potential occupational risk. A weak but statistically significant positive correlation was observed between age and average HL (r=0.202, p<0.001), suggesting that HL tends to increase with age (Fig. 1).

Fig. 1

Correlation (scatter plot) between age and average hearing loss (HL).

Risk factors associated with HL

To predict HL (dB HL) based on age group, sex, occupation, personal history, predisposing factors, type of HL, PTA final classification, and diagnosis, a multiple linear regression was conducted (Table 4). The overall regression model was statistically significant, F(28, 266)=8.84, p<0.001, explaining approximately 48.2% of the variance in HL (R²=0.482, adjusted R²=0.427). This suggests that the included variables collectively provided a good fit for predicting HL severity. The dependent variable in this model is the average hearing threshold in decibels (dB HL). Age was a strong predictor of HL, with participants aged 15–75 years showing significantly lower hearing thresholds compared to those over 75 (all p<0.05). Sex and occupational status were not significant predictors. Participants with both smoking and alcohol use (β=−18.58, p<0.001) or alcohol use alone (β=−13.02, p=0.003) had significantly greater HL. Combined noise and smoking exposure (β=23.27, p=0.004) was also significant, while noise or smoking alone was not. Among comorbidities, hypertension (β= 4.84, p=0.037), combined diabetes and hypertension (β=4.70, p=0.045), and other conditions (β=16.00, p=0.018) were significantly associated with increased HL. Unilateral HL was linked to higher thresholds than bilateral (β=13.45, p=0.002). All types of PTA-confirmed HL, i.e., CHL (β=23.44, p<0.001), SNHL (β=22.41, p<0.001), and mixed (β=38.38, p<0.001), were significantly associated with higher hearing threshold levels (dB). Clinical diagnoses such as presbycusis, chronic otitis media, noise-induced HL, sudden SNHL, and BPPV were also significantly associated with greater HL (all p<0.01), while acute suppurative otitis media was not (p=0.372).

Table 4

Multiple linear regression analysis for predicting hearing loss

Frequency-wise average pure tone thresholds by occupation and personal history (in dB HL) are shown in Table 5. Factory workers show significantly elevated thresholds at 4 kHz and 8 kHz, consistent with patterns seen in NIHL. The noise exposure + smoking group demonstrates a steep drop at 4 kHz and 8 kHz, typical of acoustic trauma or cumulative NIHL. Smoking-only individuals also show elevated thresholds compared to those without exposure. Among participants reporting occupational noise exposure, the self-reported mean (±SD) duration of exposure was 9.2 (±3.5) years, primarily among factory workers.

Table 5

Frequency-wise average pure tone thresholds by occupation and personal history

Discussion

This study investigated the degree, pattern, and risk factors of HL among adult individuals in East Singhbhum, an industrial district of eastern India. The results showed that there is a significant association between HL severity and factors such as age, industrial noise exposure, smoking, and chronic medical conditions such as diabetes and hypertension. Moreover, the present study highlights the synergistic impact of noise and smoking on auditory outcomes. The most common audiological diagnosis observed was SNHL (45.76%), followed by CHL (26.10%) among older adults and factory workers.

A striking 94.92% of participants exhibited bilateral HL, with moderate HL being most common (50.51%) in this study. These findings are consistent with existing literature, indicating that bilateral, moderate-level hearing impairments are typically observed in age-related and noise-induced cases [20]. Age was a strong predictor (p<0.001), with individuals above 60 years showing the highest prevalence of moderate to severe HL, consistent with presbycusis-related degeneration [21]. Younger age groups (15–45 years) had significantly lower hearing thresholds.

The linear trend association between participant characteristics and the severity of HL was analyzed by employing the Cochran-Armitage test. Male participants (Z=2.08, p= 0.038), those with bilateral HL (Z=−2.92, p=0.004), and factory workers (Z=2.84, p=0.005) showed increasing trends with greater hearing impairment. These findings underscore the need for targeted prevention, especially among high-risk groups like male industrial workers.

Further, factory workers significantly exhibited disproportionately higher levels of moderate and severe HL than non-factory workers and unemployed individuals (p=0.005). Although occupation alone was insignificant in multivariate regression, univariate analysis suggests a probable link between industrial noise exposure and hearing impairment. This is supported by prior studies demonstrating occupational noise as a major contributor to HL [14].

Personal history variables such as noise exposure with smoking history (β=23.27, p=0.004), smoking and alcohol (β=−18.58, p<0.001), and alcohol (β=−13.02, p=0.003) were found as significant risk factors for HL, echoing findings that it can impair cochlear blood flow and intensify the effects of noise [22].

Diabetes with hypertension was significantly associated with HL in univariate analysis (p<0.001), consistent with studies linking these conditions to cochlear microvascular damage [23,24]. However, they were not retained as independent predictors in regression analysis, suggesting an indirect role or interaction with dominant variables like age or PTA classification.

Factors associated with HL severity were identified using a regression model applied among patients with confirmed auditory impairment. As all individuals in the sample had some degree of HL, the model was not intended to assess risk for the development of HL per se. Instead, it helped to identify demographic and clinical correlates of more severe auditory dysfunction. Unequal distribution of etiologies or patient-level factors may have influenced the estimates. The PTA classification was significantly associated with hearing severity (p<0.001). Mixed HL predicted worse outcomes (β=38.38, p<0.001), while a CHL PTA result indicated higher thresholds (β=23.44, p<0.001), reinforcing PTA’s diagnostic utility [25]. Diagnoses such as SNHL (β=29.06, p<0.001), followed by BPPV (β=13.72, p=0.001), NIHL (β=10.85, p=0.007), presbycusis (β=9.66, p<0.001), and chronic otitis media (β=8.65, p=0.003) were all strongly linked to greater HL severity [26]. Moreover, it was observed that sudden SNHL was significantly associated with worse hearing outcomes. This aligns with prior studies that highlight the abrupt and often severe impact of sudden SNHL on auditory thresholds [27].

No significant sex differences were observed (p=0.038), consisting of slightly more females (52.54%) than males (47.46%), diverging from some studies that report higher male susceptibility due to occupational noise exposure [28]. This sex parity may reflect similar environmental and behavioral exposures in this study population. The weak positive correlation between age and average HL (r=0.202) aligns with existing literature, suggesting age as a contributing but not sole factor in hearing decline. The scatter plot (Fig. 1) illustrates the heterogeneity in HL among individuals of the same age. This variability may be attributed to differences in occupational exposure to noise, environmental factors, lifestyle habits, genetic predisposition, or the presence of chronic diseases such as diabetes or hypertension.

Despite limitations such as its single-center hospital setting, limited resources, lack of patient follow-up, societal barriers, and the use of convenience sampling (which may introduce selection bias and limit generalizability), this study provides valuable insight into regional patterns and risk factors of HL in an industrial setting in eastern India. A larger, community-based study is recommended to validate these findings.

This study highlights age, industrial noise exposure, smoking, and comorbidities such as hypertension with diabetes as significant risk factors for HL in the eastern Indian population. Chronic otitis media emerged as the most common diagnosis; however, the high prevalence of sensorineural and mixed HL, particularly among factory workers, underscores the impact of occupational and systemic factors. These findings call for early detection programs, stricter noise regulation, and integrated preventive strategies to mitigate HL in high-risk groups.

Notes

Conflicts of Interest

The authors have no financial conflicts of interest.

Author Contributions

Conceptualization: Srilatha Kavarthapu. Data curation: Asuri Raagini. Formal analysis: Babban Jee. Investigation: Asuri Raagini, Deepali Singh, Bhimraj Balkrishna Ramteke. Methodology: Srilatha Kavarthapu, Asuri Raagini, Deepali Singh. Project administration: Srilatha Kavarthapu. Resources: Asuri Raagini. Software: Ahammad Basha Shaik. Supervision: Srilatha Kavarthapu. Validation: Srilatha Kavarthapu. Visualization: Srilatha Kavarthapu. Writing—original draft: Srilatha Kavarthapu, Babban Jee, Ahammad Basha Shaik. Writing—review & editing: Srilatha Kavarthapu, Babban Jee. Approval of final manuscript: all authors.

Funding Statement

None

Acknowledgments

None

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Variable	Participants (n=295)	Chi-square (χ²)	p-value
Age group (yr)		65.288	<0.001*
15–30	32 (10.85)
30–45	63 (21.36)
46–60	98 (33.22)
61–75	78 (26.44)
>75	24 (8.14)
Sex		0.763	0.382
Male	140 (47.46)
Female	155 (52.54)
Occupation		73.329	<0.001*
No worker	133 (45.08)
Factory worker	133 (45.08)
Non-factory worker	29 (9.83)

Variable	Participants (n=295)	Chi-square (χ²)	p-value
Personal history		1,322.708	<0.001*
Smoking	19 (6.44)
Alcohol	11 (3.73)
Smoking & Alcohol	8 (2.71)
Noise exposure	6 (2.03)
Noise exposure + Smoking	3 (1.02)
Noise exposure + Alcohol	2 (0.68)
Bipolar disorder	3 (1.02)
Nil	243 (82.37)
Predisposing factors		173.051	<0.001*
DM	39 (13.22)
HTN	50 (16.95)
DM, HTN	61 (20.68)
Others	04 (1.36)
Nil	141 (47.80)
Diagnosis		190.610	<0.001*
ASOM	14 (4.70)
BPPV	15 (5.10)
Chronic otitis media	101 (34.20)
NIHL	23 (7.80)
Others	48 (16.30)
Presbycusis	83 (28.10)
Sudden SNHL	11 (3.70)
Type of HL		238.051	<0.001*
Unilateral	15 (5.08)
Bilateral	280 (94.92)
Level of HL		254.271	<0.001*
Normal (≤25)	8 (2.71)
Mild (26–40)	94 (31.86)
Moderate (41–60)	149 (50.51)
Severe (61–80)	35 (11.86)
Profound (>80)	9 (3.05)
PTA final		109.651	<0.001*
Normal	8 (2.71)
CHL	77 (26.10)
SNHL	135 (45.76)
Mixed	75 (25.42)

Table 3

Association between the level of hearing loss and various variables

Variable(s)	Normal (≤25 dB HL) (n=8)	Mild (26–40 dB HL) (n=94)	Moderate (41–60 dB HL) (n= 49)	Severe (61–80 dB HL) (n=35)	Profound (>80 dB HL) (n=9)	Total (n=295)	χ²/Z	p-value
Age group (yr)							37.79	<0.001*
15–30	2 (25)	16 (17.02)	12 (8.05)	1 (2.86)	1 (11.11)	32 (10.85)
31–45	4 (50)	25 (26.6)	26 (17.45)	7 (20)	1 (11.11)	63 (21.36)
46–60	2 (25)	26 (27.66)	54 (36.24)	11 (31.43)	5 (55.56)	98 (33.22)
61–75	0 (0)	23 (24.47)	47 (31.54)	7 (20)	1 (11.11)	78 (26.44)
>75	0 (0)	4 (4.26)	10 (6.71)	9 (25.71)	1 (11.11)	24 (8.14)
Sex							2.08†	0.038*
Male	5 (62.5)	39 (41.49)	68 (45.64)	23 (65.71)	5 (55.56)	140 (47.46)
Female	3 (37.5)	55 (58.51)	81 (54.36)	12 (34.29)	4 (44.44)	155 (52.54)
Occupation							2.84†	0.005*
No worker	0 (0)	44 (46.81)	74 (49.66)	11 (31.43)	4 (44.44)	133 (45.08)
Factory worker	6 (75)	36 (38.3)	62 (41.61)	24 (68.57)	5 (55.56)	133 (45.08)
Non-factory worker	2 (25)	14 (14.89)	13 (8.72)	0 (0)	0 (0)	29 (9.83)
Personal history							77.61	<0.001*
Smoking	3 (37.5)	0 (0)	9 (6.04)	7 (20)	0 (0)	19 (6.44)
Alcohol	2 (25)	6 (6.38)	3 (2.01)	0 (0)	0 (0)	11 (3.73)
Smoking & Alcohol	0 (0)	3 (3.19)	3 (2.01)	2 (5.71)	0 (0)	8 (2.71)
Noise exposure	0 (0)	3 (3.19)	3 (2.01)	0 (0)	0 (0)	6 (2.03)
Noise exposure + Smoking	0 (0)	0 (0)	0 (0)	3 (8.57)	0 (0)	3 (1.02)
Noise exposure + Alcohol	0 (0)	0 (0)	2 (1.34)	0 (0)	0 (0)	2 (0.68)
Bipolar disorder	0 (0)	0 (0)	3 (2.01)	0 (0)	0 (0)	3 (1.02)
Nil	3 (37.5)	82 (87.23)	126 (84.56)	23 (65.71)	9 (100)	243 (82.37)
Predisposing Factors							40.08	<0.001*
DM	0 (0)	13 (13.83)	20 (13.42)	6 (17.14)	0 (0)	39 (13.22)
HTN	1 (12.5)	10 (10.64)	27 (18.12)	9 (25.71)	3 (33.33)	50 (16.95)
DM, HTN	2 (25)	7 (7.45)	40 (26.85)	12 (34.29)	0 (0)	61 (20.68)
Others	0 (0)	1 (1.06)	3 (2.01)	0 (0)	0 (0)	4 (1.36)
Nil	5 (62.5)	63 (67.02)	59 (39.6)	8 (22.86)	6 (66.67)	141 (47.8)
Type of HL							−2.92†	0.004*
Unilateral	3 (37.5)	3 (3.19)	5 (3.36)	3 (8.57)	1 (11.11)	15 (5.08)
Bilateral	5 (62.5)	91 (96.81)	144 (96.64)	32 (91.43)	8 (88.89)	280 (94.92)
PTA final							352.7	<0.001*
Normal	8 (100)	0 (0)	0 (0)	0 (0)	0 (0)	8 (2.71)
CHL	0 (0)	34 (36.17)	43 (28.86)	0 (0)	0 (0)	77 (26.10)
SNHL	0 (0)	56 (59.57)	59 (39.6)	18 (51.43)	2 (22.22)	135 (45.76)
Mixed	0 (0)	4 (4.26)	47 (31.54)	17 (48.57)	7 (77.78)	75 (25.42)
Diagnosis							205.1	<0.001*
ASOM	6 (75)	2 (2.13)	4 (2.68)	1 (2.86)	1 (11.11)	14 (4.75)
BPPV	0 (0)	1 (1.06)	5 (3.36)	6 (17.14)	3 (33.33)	15 (5.08)
Chronic otitis media	0 (0)	34 (36.17)	62 (41.61)	4 (11.43)	1 (11.11)	101 (34.24)
NIHL	2 (25)	10 (10.64)	9 (6.04)	0 (0)	2 (22.22)	23 (7.8)
Others	0 (0)	28 (29.79)	18 (12.08)	2 (5.71)	0 (0)	48 (16.27)
Presbycusis	0 (0)	16 (17.02)	51 (34.23)	14 (40)	2 (22.22)	83 (28.14)
Sudden SNHL	0 (0)	3 (3.19)	0 (0)	8 (22.86)	0 (0)	11 (3.73)

Values are presented as n (%).

p<0.05, statistically significant;

^†

two-sided p-values for the Cochran-Armitage trend test (z-value).

DM, diabetes mellitus; HTN, hypertension; ASOM, acute suppurative otitis media; BPPV, benign paroxysmal positional vertigo; NIHL, noise-induced hearing loss; SNHL, sensorineural hearing loss; CHL, conductive hearing loss; PTA, pure tone audiometry.

Table 4

Multiple linear regression analysis for predicting hearing loss

Predictor	Estimate (β)	SE	95% CI		t value	p-value

			Lower	Upper
Intercept	18.34	7.54	3.50	33.17	2.43	0.016*
Age group (yr)
15–30 (vs. >75)	−11.35	4.09	−19.41	−3.30	−2.78	0.006*
30–45 (vs. >75)	−10.89	3.60	−17.98	−3.80	−3.03	0.003*
46–60 (vs. >75)	−6.97	3.22	−13.32	−0.62	−2.16	0.031*
61–75 (vs. >75)	−11.07	3.17	−17.31	−4.82	−3.49	<0.001*
Sex
Male (vs. Female)	0.77	2.89	−4.91	6.45	0.27	0.790
Occupation
Non-factory worker (vs. No worker)	−0.28	3.48	−7.13	6.57	−0.08	0.936
Factory worker (vs. No worker)	3.17	3.04	−2.81	9.15	1.04	0.297
Personal history
Smoking (vs. Nil)	−2.30	3.38	−8.96	4.36	−0.68	0.497
Alcohol (vs. Nil)	−13.02	4.41	−21.71	−4.34	−2.95	0.003*
Smoking & Alcohol (vs. Nil)	−18.58	5.42	−29.26	−7.90	−3.43	<0.001*
Noise exposure (vs. Nil)	−9.42	5.85	−20.93	2.10	−1.61	0.109
Noise exposure + Alcohol (vs. Nil)	−17.98	10.82	−39.29	3.33	−1.66	0.098
Noise exposure + Smoking (vs. Nil)	23.27	7.96	7.61	38.94	2.92	0.004*
Bipolar disorder (vs. Nil)	0.28	8.12	−15.70	16.26	0.03	0.973
Predisposing factors
DM (vs. Nil)	3.13	2.65	−2.09	8.35	1.18	0.239
HTN (vs. Nil)	4.84	2.31	0.29	9.39	2.10	0.037*
DM, HTN (vs. Nil)	4.70	2.34	0.10	9.31	2.01	0.045*
Others (vs. Nil)	16.00	6.71	2.79	29.20	2.38	0.018*
HL type
Unilateral (vs. Bilateral)	13.45	4.29	5.00	21.90	3.14	0.002*
PTA final
CHL (vs. Normal)	23.44	6.49	10.66	36.21	3.61	<0.001*
SNHL (vs. Normal)	22.41	6.22	10.16	34.66	3.60	<0.001*
Mixed (vs. Normal)	38.38	6.21	26.16	50.59	6.18	<0.001*
Diagnosis
Presbycusis (vs. Others)	9.66	2.75	4.24	15.09	3.51	<0.001*
Chronic otitis media (vs. Others)	8.65	2.88	2.97	14.32	3.00	0.003*
NIHL (vs. Others)	10.85	4.00	2.97	18.73	2.71	0.007*
ASOM (vs. Others)	4.64	5.19	−5.58	14.87	0.89	0.372
Sudden SNHL (vs. Others)	29.06	4.61	19.99	38.13	6.31	<0.001*
BPPV (vs. Others)	13.72	4.13	5.59	21.84	3.32	0.001*

Multiple linear regression analysis was performed to assess predictors of HL (in dB HL). The reference categories for each variable were: age group=more than 75 years; gender=female; occupation=no worker; personal history=nil; predisposing factors=nil; type of HL=bilateral; PTA diagnosis=normal hearing; clinical diagnosis=others.

p<0.05, statistically significant.

HL, hearing loss; DM, diabetes mellitus; HTN, hypertension; ASOM, acute suppurative otitis media; BPPV, benign paroxysmal positional vertigo; NIHL, noise-induced HL; SNHL, sensorineural HL; CHL, conductive HL; PTA, pure tone audiometry; SE, standard error; CI, confidence interval.

	Average pure tone thresholds (dB HL)

	250 Hz	500 Hz	1,000 Hz	2,000 Hz	4,000 Hz	8,000 Hz
Occupation
No worker (n=133)	25.1	27.3	30.2	34.8	43.6	41.1
Factory worker (n=133)	29.4	33.6	36.1	41.9	58.3	56.7
Non-factory worker (n=29)	24.2	26.5	28.7	32.4	39.5	38.9
Personal history
Smoking only (n=19)	28.9	31.2	34.5	38.7	50.1	48.3
Noise exposure + Smoking (n=3)	36.7	40.5	44.2	52.1	71.3	69.2
No exposure (n=243)	24.5	26.1	28.9	32.6	37.5	35.9