Silent threat: Prevalence of obstructive sleep apnea and its risk factors among adults in a block of West Bengal

ABSTRACT

Background:

Obstructive sleep apnea (OSA) is a prevalent yet underdiagnosed sleep disorder characterized by repeated upper airway obstruction during sleep, contributing to significant health risks. While global data highlight its high prevalence, evidence from rural Indian populations remains limited.

Objectives:

To estimate the prevalence of people at risk of OSA among adults (≥18 years) residing in Budge-Budge II block of West Bengal and to identify associated socio-clinical risk factors, if any.

Methods:

This community-based study was conducted from November 2024 to January 2025 in Budge-Budge II Block, West Bengal. A sample of 205 adults was selected using multistage random sampling. Data were collected through household visits using a pre-tested structured schedule that included the modified Berlin questionnaire for OSA risk assessment. Medical records were reviewed; anthropometric measurements and blood pressure recordings were obtained. Statistical analysis included descriptive statistics and logistic regression to identify risk factors.

Results:

The mean age of the participants was 43.35 ± 16.30 years, and 54% were female. The overall prevalence of high risk for OSA was 29%, with a higher proportion among males (57.6%). Hypertension was present in 31.7% of the participants, while 40% had a body mass index above the normal range. Multivariate analysis identified significant associations (P < 0.05) between high risk for OSA and factors such as advanced age, male gender, hypertension, obesity, and regular alcohol consumption.

Conclusion:

The study observed a substantial burden of undiagnosed OSA risk in rural West Bengal. Given the association with modifiable risk factors, community-level interventions, including the training of health workers for early screening using tools like the modified Berlin questionnaire and prompt referral, can aid in reducing the public health impact of OSA in underserved areas.

Introduction

Obstructive sleep apnea (OSA) is characterized by episodes of complete or partial airway collapse due to the relaxation of upper respiratory muscles during sleep, causing soft tissue in the throat to collapse and block the upper airway. This leads to intermittent hypoxia (IH), sleep fragmentation, and excessive daytime sleepiness.[1] The condition represents a significant public health challenge due to its association with multiple comorbidities and complications.

OSA is independently associated with an increased likelihood of hypertension, coronary artery disease, heart failure, type 2 diabetes mellitus, and cognitive decline. The underlying pathophysiological mechanisms include IH leading to neuroinflammation, metabolic dysregulation, hypoperfusion, and endothelial dysfunction, all of which contribute to cardiovascular risk and neurocognitive impairment in individuals with OSA.[2,3]

Global epidemiologic studies have demonstrated that the prevalence of OSA varies widely from 9% to 38% in the general adult population.[4] In 2019, Benjafield et al. performed a global review of OSA prevalence and concluded that almost one billion adults worldwide suffer from the disease.[5] The prevalence appears to be increasing, likely due to the global obesity epidemic, aging populations, and improved diagnostic awareness.[6]

India, with its diverse population exceeding 1.4 billion, presents potential geographical heterogeneity in OSA prevalence. A systematic review and meta-analysis of OSA prevalence in Indian adults by Suri et al. reported a pooled prevalence of 17%, with higher rates observed in urban populations (15%–36%) compared to rural areas (8%–19%).[7] Other Indian studies have shown varying prevalence rates: 19.5% in urban Delhi,[8] 9.3% in semi-urban South India,[9] and 12.4% in rural Maharashtra.[10]

OSA poses significant public health risks as it is strongly associated with increased incidence of cardiovascular diseases and cerebrovascular diseases, type 2 diabetes, and road traffic accidents due to excessive daytime sleepiness, leading to reduced quality of life and higher healthcare burden. Despite its high prevalence and associated morbidity and mortality, OSA remains widely underdiagnosed and undertreated due to nonspecific symptoms and limited awareness among the Indian population, since daytime sleepiness and snoring are often considered commonplace.[11]

However, OSA is a treatable condition, and early diagnosis and management can help prevent associated morbidity and complications. Effective interventions range from lifestyle modifications to continuous positive airway pressure therapy and surgical approaches.[12] However, there is a paucity of studies on the prevalence of people at risk of OSA in the rural population of India, particularly in West Bengal.

This study aims to estimate the prevalence of people at risk of OSA and associated socio-clinical factors in a block of West Bengal, which could inform public health interventions for this significant yet often overlooked health condition.

Methods

Study type, design, duration, and setting

This was an observational cross-sectional study conducted for 3 months from November 2024 to January 2025 in selected sub-centres of Budge-Budge II Block.

Study population

Adult residents (aged 18 years and above) of the area were included in the study.

Exclusion criteria

Individuals who slept alone in their room (as certain questions in the Berlin questionnaire require room partner’s observations) and those suffering from severe physical and mental illness at the time of data collection were excluded from the study.

Sample size calculation

The sample size was calculated based on a previous study by Goyal et al., which observed a prevalence of moderate to severe of OSA as 32.5% among adult population of central India, Applying Cochran’s formula: (Z² × pq)/d², where P = 0.325, q = 1 − P = 0.675, Z = 1.96 at 95% confidence interval (CI), absolute error (d) =10%, sample size was = 85.[13]

After multiplying by a design effect of 2 for multi-stage sampling and adding 20% for nonresponse, the minimum sample size determined was 204. However, a total of 205 samples were collected.

Sampling technique

Multistage random sampling technique was employed to select the study participants (n = 205) as shown in Figure 1.

Figure 1.

Multistage random sampling technique showing the process of selection of study participants (n = 205)

Study tools

1. A predesigned, pretested, structured schedule comprising sociodemographic profile and modified Berlin questionnaire for OSA (validated in the Indian population)[14]

2. Aneroid sphygmomanometer for blood pressure monitoring

3. Nonstretchable measuring tape for measurement of height

4. Weighing machine for measurement of body weight.

Study technique

• A door-to-door home visit was conducted, and one adult from each household selected by the Kish-grid method was interviewed after obtaining informed consent.

• Medical records were reviewed, and ongoing medications were checked to assess the status of hypertension, diabetes mellitus, and chronic upper airway diseases.

• Blood pressure was measured by the auscultatory method with a standardized aneroid sphygmomanometer and a cuff covering at least 80% of the upper arm. After a 10-min seated rest (feet flat, arm at heart level), two readings were taken and averaged. If elevated blood pressure was detected on the first measurement, two more readings were obtained 10 min apart, and the average of the two closest readings was recorded. The participant was asked to stand upright, barefoot, with feet together, back straight, and head facing directly ahead.

Study variables

Dependent variable

• Risk of OSA (high risk/low risk as per modified Berlin questionnaire).

Independent variables

• Sociodemographic characteristics (age, gender, nature of physical activity, etc.)

• Clinical characteristics (hypertension, diabetes mellitus, chronic upper airway diseases)

• Behavioral factors (addiction to smoking and alcohol)

• Anthropometric measurements (body mass index [BMI]).

Ethical considerations

Ethical clearance was obtained from the Institutional Ethics Committee of IPGME&R (vide memo: IPGME&R/IEC/2024/0851). Informed consent was obtained from all study participants.

Data analysis

Data were compiled in Microsoft Office Excel 2021 and analyzed using IBM SPSS Statistics for Windows, Version 25.0 (IBM Corp., Armonk, NY, USA). Analysis of the modified Berlin questionnaire was performed as per standard guidelines to categorize participants into high risk and low risk for OSA.

Continuous variables (e.g., age) were summarized using mean and standard deviation, while categorical variables (e.g. gender, education level, BMI categories, and comorbidities) were presented as frequencies and percentages.

Univariate logistic regression was performed for all independent variables, and those with P < 0.2 or biologically plausible were included in multivariate logistic regression (P < 0.05 considered significant) to analyze the effect of risk factors on developing risk of OSA.

Operational definitions

1. OSA risk assessment: Based on the modified Berlin questionnaire, which consists of three categories that assess symptoms and risk factors associated with OSA:

   • Category 1 assesses snoring and witnessed apneas, with a positive score given for frequent loud snoring and/or apneas occurring more than three to four times per week.

   • Category 2 evaluates daytime sleepiness or fatigue, with a positive score if symptoms occur more than three to four times per week or if the participant has ever fallen asleep while watching TV or traveling for a short distance in a vehicle or awaiting in a queue.

   • Category 3 considers the presence of hypertension and/or obesity (BMI ≥25 kg/m²), scoring positive if either is present. Participants are classified as high risk for OSA if two or more categories are positive; otherwise, they are considered low risk.

2. Hypertension: Participants aged ≥60 years with systolic blood pressure (SBP) ≥150 mmHg or diastolic blood pressure (DBP) ≥90 mmHg and those aged <60 years with SBP ≥140 mmHg or DBP ≥90 mmHg were classified as hypertensive as per JNC 8,[15] along with participants already on antihypertensive medication

3. Chronic upper airway disease refers to persistent or recurrent inflammation of the upper respiratory tract—particularly the nose, sinuses, and pharynx—lasting 12 weeks or more, and typically includes conditions such as chronic rhinosinusitis, allergic rhinitis, and nasal polyposis, characterized by symptoms such as nasal congestion, rhinorrhea, facial pressure, and/or loss of smell, as per the EPOS 2020 guidelines.[16] A case of chronic upper airway disease was defined as a person reporting ≥2 of the four core symptoms (at least one being nasal obstruction or nasal discharge) persisting for ≥12 weeks, based on self-report via a structured questionnaire or documentation in medical records.

Results

The mean age of the study participants was 43.35 ± 16.30 years. Females constituted 54% of the study population; 39.5% had completed primary education; 56% were sedentary workers, and most participants (91.7%) were currently married. One-fifth (20%) belonged to the lower socioeconomic class. In terms of lifestyle behaviors, 24.9% reported current alcohol consumption, and 28.8% were current smokers [Table 1].

Table 1.

Distribution of the study participants according to their sociodemographic profile (n=205)

Sociodemographic profile	n (%)
Age
≤40	104 (50.7)
>40	101 (49.3)
Gender
Female	111 (54.1)
Male	94 (45.9)
Religion
Hinduism	167 (81.5)
Islam	38 (18.5)
Educational status
Illiterate	18 (8.8)
Primary	81 (39.5)
Middle	62 (30.2)
Secondary and above	44 (21.5)
Nature of physical activity
Sedentary	115 (56.1)
Moderate	79 (38.5)
Heavy	11 (5.4)
Marital status
Currently married	188 (91.7)
Never married	8 (3.9)
Separated/widowed	9 (4.4)
Socioeconomic status as per (Modified BG Prasad Scale 2024)
Upper-middle class	38 (18.5)
Middle class	65 (31.7)
Lower-middle class	61 (29.8)
Lower class	41 (20.0)
Current alcohol consumer
No	154 (75.1)
Yes	51 (24.9)
Current smoker
No	146 (71.2)
Yes	59 (28.8)

Regarding clinical characteristics, 31.7% of participants were hypertensive, 31.2% reported having chronic upper airway disease, and 28.8% had diabetes mellitus. Overall, 40% had a BMI above the normal range as per the Asian-Indian classification—of these, 26% were overweight, 13% were classified as Obese Class I, and 1% as Obese Class II. [Table 2].

Table 2.

Distribution of the study participants according to their clinical profile (n=205)

Clinical profile	n (%)
Hypertension
Absent	140 (68.3)
Present	65 (31.7)
Diabetes mellitus
Absent	146 (71.2)
Present	59 (28.8)
Chronic upper airway disease
Absent	141 (68.8)
Present	64 (31.2)
BMI (as per Asian-Indian classification)
Within normal range (18.5–22.9 kg/m2)	123 (60)
Overweight (23–24.9)	53 (26)
Obese I (25–29.5)	26 (13)
Obese II (>29.5)	3 (1)

Based on the modified Berlin questionnaire, 29% of the participants were identified as being at high risk for OSA, with a male predominance of 57.6% [Figure 2].

Figure 2.

Pie of pie diagram showing distribution of study participants according to risk of obstructive sleep apnea (n = 205)

Multivariate logistic regression analysis revealed significant associations between high-risk OSA and several socioclinical factors. These included advanced age (adjusted odds ratio [aOR]: 2.29; 95% CI: 1.11–6.69; P = 0.006), male gender (aOR: 1.94; 95% CI: 1.06–6.70; P = 0.024); alcohol consumption (aOR: 2.83; 95% CI: 1.85–3.92; P = 0.035), hypertension (aOR: 5.17; 95% CI: 1.49–8.34; P = 0.001), and BMI above the normal range (aOR: 3.26; 95% CI: 1.37–7.71; P = 0.008) [Table 3].

Table 3.

Association of risk of obstructive sleep apnea with socioclinical factors of the study participants: Binary logistic regression (n=205)

Socioclinical factors	OSA		OR	P	aOR	P
	Low risk	High risk
Age
≤40	89 (85.6)	15 (14.4)	1		1
>40	57 (56.4)	44 (43.6)	3.22 (1.11–8.43)	0.079	2.29 (1.11–6.69)	0.006
Gender
Female	86 (77.5)	25 (22.5)	1		1
Male	60 (63.8)	34 (36.2)	3.51 (1.28–8.95)	0.033	1.94 (1.06–6.70)	0.024
Hypertension
Absent	116 (82.9)	24 (17.1)	1		1
Present	30 (46.2)	35 (53.8)	8.82 (3.47– 22.40)	0.001	5.17 (1.49–8.34)	0.001
Chronic upper airway disease
Absent	108 (76.6)	33 (23.4)	1		1
Present	38 (59.4)	26 (40.6)	3.44 (2.23–7.84)	0.10	1.18 (0.32–1.65)	0.127
Current alcohol consumer
No	124 (80.5)	30 (15.8)	1		1
Yes	22 (43.1)	29 (56.9)	2.18 (1.09–4.36)	0.048	2.83 (1.85–3.92)	0.035
Body mass index
<25	96 (78.0)	27 (22.0)	1		1
≥25	50 (61.0)	32 (39.0)	7.38 (4.20–10.71)	0.003	3.26 (1.37–7.71)	0.008

P<0.05 was taken as significant, Model fitness information: Cox and Snell R²=0.43, Nagelkerke R²=0.57, Omnibus Test of Model coefficients were significant (P<0.001) and Hosmer–Lemeshow Goodness of Fit Test was not significant (P=0.070), suggesting a good fit of the model. OR=Odds ratio, OSA=Obstructive sleep apnea, aOR=Adjusted odds ratio

Discussion

This study found that 29% of the rural adult population was at high risk for OSA, with a male predominance. This prevalence is comparable to findings from other studies conducted in rural Indian settings. Das et al. reported a 25% prevalence of high OSA risk in a rural community of Odisha, India, using the Berlin questionnaire.[17]

The higher prevalence among males observed in our study aligns with most contemporary Indian studies. Priyadarshini et al. documented a male-to-female ratio of 3:2 for moderate-to-severe OSA in their study.[18] This gender disparity is attributed to differences in upper airway anatomy, hormonal influences, and fat distribution patterns.

The significant association between advanced age and OSA risk in our study is consistent with findings of a study conducted by Devaraj et al., which reported that advanced age increased the odds of OSA.[19] The age-related increased risk is attributed to changes in upper airway muscle tone, increased pharyngeal fat deposition, and reduced lung volumes.

Regarding BMI, our finding that increased BMI is significantly associated with OSA risk corroborates results from several recent Indian studies. Ghosh et al. reported that obesity (BMI >25 kg/m² as per Asian criteria) increased the odds of OSA by 3.4 times in their study.[20] The relationship between obesity and OSA involves fat deposition in upper airway structures, reduced functional residual capacity, and altered ventilatory control.

The significant association between hypertension and OSA risk was observed in our study. Similarly, a semi-urban Lucknow population-based study found that 62.4% of individuals at high risk for OSA had hypertension, which was identified as an independent risk factor.[21] The bidirectional relationship between OSA and hypertension involves mechanisms such as sympathetic activation, oxidative stress, and endothelial dysfunction. Recent work by Tripathi et al. demonstrated that OSA treatment led to improved blood pressure control in previously resistant hypertensive patients, highlighting the interlinked pathophysiology.[22]

Singh et al. reported a significant association of high risk of OSA with regular alcohol consumption in their community-based study done in a semi-urban setting, which is similar to the finding of our observation.[23] Alcohol consumption can exacerbate OSA by reducing upper airway muscle tone and increasing airway collapsibility.

Interestingly, our study did not find a significant association between chronic upper airway diseases and OSA risk in the multivariate analysis, contrary to the many existing Indian literatures. This discrepancy could be attributed to potential underdiagnosis of chronic upper airway conditions in rural settings or differences in the severity of these conditions among our study participants.

Conclusion

The findings of this community-based cross-sectional study highlight a substantial burden of undiagnosed risk for OSA among rural adults in Budge-Budge II Block, West Bengal, with nearly one-third (29%) of participants identified as high risk. The study not only confirms established associations between OSA risk and advanced age, male gender, hypertension, elevated BMI, and regular alcohol consumption but also provides robust evidence from a rural Indian setting—a demographic that is often underrepresented in OSA research in India, despite being home to a significant proportion of the country’s population. By employing a validated risk assessment tool (modified Berlin questionnaire) and a rigorous multistage random sampling approach, the study addresses critical gaps in knowledge regarding the burden of OSA and its determinants in rural communities. Importantly, the study highlights the urgent need for increased routine screening for OSA in rural India, where lack of awareness and limited access to specialized care contribute to underdiagnosis and undertreatment. These features make the study a valuable contribution to the literature on OSA in India.

However, polysomnography, the gold standard test for diagnosing OSA, could not be performed due to the unavailability of the equipment in nearby health facilities as well as constraints related to manpower.

Recommendations

Given the high prevalence of OSA risk and its association with modifiable factors, integrating simple screening tools for OSA into existing rural health programs—such as those under the National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Diseases and Stroke—should be considered.[24] Training frontline health workers to recognize OSA risk and facilitate early referral can help mitigate the public health impact of this condition.

Conflicts of interest

There are no conflicts of interest.

Funding Statement

Nil.