Next Article in Journal
Changes in Upper Airway Airflow After Rapid Maxillary Expansion Beyond the Peak Period of Adenoidal Growth—A CBCT Study Using Computer Fluid Dynamics and Considering Adenoidal Dimensions as a Factor
Next Article in Special Issue
Balancing the Oral Redox State: Endogenous and Exogenous Sources of Reactive Oxygen Species and the Antioxidant Role of Lamiaceae and Asteraceae
Previous Article in Journal
Factors Influencing the Selection of Materials and Luting Agents for Single-Crown Restorations
Previous Article in Special Issue
The Relationship Between the Perceived Personality Traits of Dentists, Dental Anxiety, Negative Stories, and Negative Experiences with Dental Treatment: A Cross-Sectional Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Dentistry Journal
Volume 13
Issue 5
10.3390/dj13050208
dentistry-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Screening for Obstructive Sleep Apnea Among the Adult Population in the Northeastern Region of Romania

by
Olja Tanellari
1,
Adela Alushi
2,
Celiana Toti
3,
Carina Balcos
4,
Adina Oana Armencia
4,*,
Tinela Panaite
1 and
Irina Zetu
1
1
Department of Orthodontics, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
2
Department of Orthodontics, Aldent University, 1005 Tirana, Albania
3
Department of Orthodontics, Faculty of Dental Medicine, University of Medicine, Tirana (UMT), 1005 Tirana, Albania
4
Department of Surgery, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
*
Author to whom correspondence should be addressed.
Dent. J. 2025, 13(5), 208; https://doi.org/10.3390/dj13050208
Submission received: 5 March 2025 / Revised: 2 May 2025 / Accepted: 6 May 2025 / Published: 12 May 2025
(This article belongs to the Special Issue Preventive Dentistry and Dental Public Health)
Browse Figures
Versions Notes

Abstract

:
Background: Sleep apnea is a breathing affliction that affects sleep quality, with significant implications for overall physical and oral health, as well as mental health. Aim: The aim of this study was to evaluate the validity of the STOP-Bang and Epworth questionnaires as preoperative screening tools for obstructive sleep apnea (OSA) in the adult population from the NE region of Romania. Materials and Methods: A total of 222 participants were selected. The questionnaire method was used, with the subjects completing the STOP-Bang and Epworth questionnaires. A chi-squared test, an ANOVA, and Student’s t-tests were used for the statistical analysis. Results: the STOP-Bang questionnaire revealed an increased risk of OSA among those with a high BMI, an age over 50 years, or a large neck circumference. Regarding the Epworth questionnaire, daytime sleepiness was more frequent among obese individuals and those with associated pathologies. Significant correlations were found between OSA and obesity, age, and associated pathologies, with limited observations on the influence of gender on the risk of OSA. Conclusions: The studied questionnaires are effective and easy-to-use tools for the preoperative screening of OSA, demonstrating a significant correlation between the identified risk factors and the disease severity.

1. Introduction

Obstructive sleep apnea (OSA) is marked by repeated airflow cessation during sleep due to upper airway obstruction, leading to frequent arousals as the patient struggles to breathe. Early screening is essential, as undiagnosed OSA can be worsened by analgesic, anesthetic, and sedative drugs. The risk factors include age, the male gender, menopause, anatomical anomalies, heredity, smoking, alcohol use, and obesity. If left untreated, OSA can cause symptoms such as nocturnal choking, morning headaches, and daytime sleepiness, and is associated with cardiovascular disease, depression, and other health conditions [1,2,3,4].
OSA is associated with serious outcomes such as cardiovascular disease, diabetes, and an increased mortality [1,5,6,7,8,9,10,11,12,13,14,15,16,17]. Despite a prevalence of 2–7%, it remains widely underdiagnosed—especially in surgical patients—raising postoperative risks [3,4,5,6,7,8,9,10,11,12].
The early screening for OSA, including in dental practices, is essential and can be effectively carried out using simple tools such as the STOP-Bang questionnaire, STOP, Berlin, and the P-SAP score [11,12,18,19]. Given the increased risk of perioperative complications in undiagnosed patients [7,20,21,22], the use of the STOP-Bang questionnaire is particularly valuable, as it combines symptom-based and demographic elements, offering a faster and more practical screening approach compared to other tools [23,24,25]. So, the aim of this study was to determine the validity of the STOP-Bang and Epworth questionnaires as preoperative screening tools by identifying individuals who are predisposed to developing a severe form of OSA in the population from the NE region of Romania.

2. Materials and Methods

2.1. Study Design and Population

A cross-sectional study was conducted between January 2022 and January 2023, with the approval of the Institutional Ethics Committee of the “Grigore T. Popa” University of Medicine and Pharmacy in Iași, Romania (No. 188/25 May 2022), in accordance with the Hel Declaration. The initial group consisted of 350 patients attending private dental practices in the northeastern region of Romania. The following inclusion criteria were used to select the study participants: patients willing to complete the questionnaires, those over 40 years of age, both females and males, and those presenting risk factors for obstructive sleep apnea (increased weight, persistent drowsiness and fatigue, unsatisfactory sleep, etc.). Incomplete questionnaires, patients who did not present risk factors for sleep apnea, and participants who did not live in the northeastern region of Romania were excluded from this study.
At the beginning of the survey, the participants were provided with information about the research and its objectives. The completion of the questionnaire was considered an indication of consent to participate. Participation in the study was entirely voluntary and anonymous, and no identifying data were collected.
From the patient’s chart, we collected data related to the patient’s type/class of dento-maxillary anomaly, their neck circumference measured in cm, and their body mass index (BMI). BMI is a measure of body fat according to height and weight, applicable to both men and women. The BMI classifications are as follows: underweight (<18.5), normal weight (18.5–24.9), overweight (25–29.9), and obese (≥30).

2.2. Instruments

Two questionnaires were used to collect data:

2.2.1. The STOP-Bang Questionnaire

The STOP-Bang questionnaire is a sensitive and easy-to-administer screening tool for OSA [6]. Originally applied for preoperative screening, the STOP-Bang questionnaire has been subsequently validated in various populations [26,27,28,29].
The questionnaire contains 8 questions that are answered with “Yes”/”No”. The risk of OSA is considered low when 0–2 questions are answered positively. The risk is considered medium when the subject answers positively to 3–4 questions and high when the participant selects the answer of “Yes” for 5–8 questions; also, the risk is increased when the subject answers “Yes” to 2 or more of the first 4 questions; in male subjects who answer “Yes” to 2 or more of the first 4 questions when associated with a BMI > 35 kg/m2; or “Yes” to 2 or more of the first 4 questions associated with neck circumference (43 cm in men, 41 cm in women) [26,27,28,29].

2.2.2. Epworth Questionnaire—Daytime Sleepiness Scale (ESS)

The Epworth sleepiness scale (ESS) assesses excessive daytime sleepiness—a symptom due to its direct impact on quality of life and the risk of road accidents, and an indicator of poor sleep quality in patients with OSA [29,30,31,32].
The questionnaire contains 8 questions that participants must answer by choosing an answer option as follows: 0 = do not doze/fall asleep, 1 = low probability of dozing/falling asleep, 2 = moderate probability of dozing/falling asleep, or 3 = high probability of dozing/falling asleep. The final score represents the sum of the chosen scores. A score between 10 and 17 points indicates pathological drowsiness, and a score higher than 18 is recorded in subjects with a state of accentuated drowsiness. If the score obtained in this test exceeds the value of 10, consultation with a sleep specialist is recommended [32].

2.3. Statistical Analysis

For the data analysis, the SPSS 26.0 for Windows program was used, with the aim of highlighting the results, which are presented in the form of frequencies, mean values, and standard deviations. An ANOVA and Student’s t-test were used for the comparison of variables. The statistical significance was set at a value of p = 0.05.

3. Results

3.1. Participant Characteristics

Of the 350 questionnaires distributed, only 222 questionnaires were submitted correctly and completely. The analysis of demographic data indicated that 59% of these participants were male subjects and the mean age of the group was 61.25 years (SD = 7.165) (minimum age: 42 years, maximum age: 75 years). More than half of the subjects came from urban areas (68%) and 56.3% of them were of a high socio-economic level (Table 1).

3.2. Analysis of the STOP-Bang Questionnaire

The STOP-Bang responses indicated strong associations with obesity and comorbidities, while gender showed minimal influence. Loud snoring, daytime sleepiness, and sleep apnea were significantly more common in obese individuals and those with comorbidities (p < 0.01). Hypertension was linked to comorbidities (p = 0.009), and an age > 50 was correlated with a high BMI and comorbidities (p = 0.000). A neck circumference > 41 cm was more frequent in men, obese subjects, and those with comorbidities. A high OSA risk was strongly associated with obesity (74.7%) and comorbidities (56%) (Table 2).
Multinomial regression (Table 3) identified the female gender (β = 0.80, p = 0.000), an age under 50 (β = 5.45, p = 0.000), and BMI (β = 10.1, p = 0.000) as significant predictors of OSA. Age and BMI significantly influenced the OSA risk. Individuals over 50 had a 5.5-fold higher likelihood of OSA (Exp(B) = 5.455, CI: 1.614–18.441, p = 0.006), while age had no significant impact on the medium-risk STOP-Bang group (p = 0.062). Overweight individuals (BMI of 25–30) had a 1.7-fold increased OSA risk, rising to over 10-fold in obese subjects (BMI ≥ 30; Exp(B) = 10.147, CI: 4.422–23.283, p = 0.000).

3.3. Epworth Questionnaire (ESS) Analysis

An analysis of the responses regarding the likelihood of dozing off in everyday situations revealed significant associations with BMI and comorbidities, while gender showed a less consistent influence. When reading, 62.6% of women and 38.2% of men reported a moderate chance of dozing off, which was significantly correlated with obesity (39.7%) and comorbid conditions (27.1%, p = 0.000). Watching TV was associated with moderate drowsiness in 56% of overweight and 63.7% of obese participants, with significant differences by gender, BMI, and comorbidities (p = 0.000).
In passive public environments, drowsiness was more frequent among women (78%), again linked to an increased BMI and comorbidities. During long car rides, 60.4% of women and 35.1% of men reported a high likelihood of dozing off, especially those with obesity and associated health issues (p = 0.000).
Post-meal drowsiness was also more common among women (62.6%), and was correlated with excess weight and comorbidities. Although less frequent, drowsiness while talking was significantly associated with BMI. Similarly, sleepiness during driving breaks was more commonly reported by individuals with an elevated BMI and comorbid conditions (p = 0.000) (Table 4).
The distribution of subjects by the obstructive sleep apnea (OSA) risk and the dento-maxillary anomaly (ADM) class, based on the STOP-Bang scores, showed a trend toward an increased OSA risk for classes II and III. Among those with class I ADM, 18.1% had a low risk, 34.7% had a medium risk, and 47.2% had a high risk. In class II, the proportion with a high risk rose to 60%, while low-risk cases decreased to 17.5%. In class III, a high risk was observed for 68.4% of the participants, with only 7.9% classified as low-risk (Figure 1).
An analysis of the distribution of daytime sleepiness across dento-maxillary anomaly (ADM) classes suggests a trend toward increased symptom severity with more advanced anomalies. The proportion of subjects reporting moderate sleepiness (scores 8–9) remained relatively stable across classes, ranging from 15.8% for class III to 20.8% for class I. Similarly, the percentage of those with low sleepiness (scores 0–7) was consistent across groups, with a slight increase observed for class III (13.2%) (Figure 2).

4. Discussion

While most OSA screening studies have focused on clinical populations, this study evaluated the predictive value of the STOP-Bang and Epworth tools in a general adult population from northeastern Romania—an underrepresented region in the literature. It also highlighted the underutilized role of dental professionals in OSA screening, offering a novel interdisciplinary perspective.

4.1. Key Predictive Risk Factors

This study confirmed the predictive relevance of key OSA risk factors, obesity, age, and an increased neck circumference—identified via the STOP-Bang questionnaire. Also, elevated Epworth scores further reflected excessive daytime sleepiness, a hallmark of OSA [25,31].
Several systemic conditions, including hypertension, diabetes, menopause, and skeletal anomalies, have been associated with an increased OSA risk [25,33,34,35,36,37,38,39]. The data from this study support the assertion that hypertension and other associated conditions (e.g., diabetes) are common among patients with OSA, particularly in its severe forms, and these associations are statistically significant.
Gender plays an important role in OSA. Although, in our study, gender showed a limited influence on the OSA risk according to the STOP-Bang scores, the regression analysis identified the female gender as a significant predictor, possibly due to differences in symptom reporting. Women often report symptoms such as insomnia, fatigue, and mood disturbances, largely influenced by hormonal changes [4,40,41,42,43]. In men over 55, OSA often coexists with obesity, diabetes, cardiovascular disease, and greater pharyngeal collapsibility [42,44,45,46].
Obesity is a major and bidirectional factor in OSA, and was confirmed by this study as the strongest predictor of the condition, influencing both the symptom severity and the overall risk, while also contributing to a cycle in which OSA promotes further weight gain through metabolic dysregulation and reduced physical activity, with a higher BMI and ENT-related disorders amplifying the disease severity [47,48,49,50,51].

4.2. Screening Tool Performance and Practicality

This study showed that both STOP-Bang and Epworth are effective screening tools for OSA, with STOP-Bang proving more efficient in identifying the overall risk and Epworth better reflecting the daytime symptom impact. STOP-Bang proved more efficient when the score differences were notable, especially since daytime symptoms like fatigue and reduced functioning often outweigh nocturnal signs in OSA patients [51].
Other studies have shown that the STOP-Bang questionnaire has strong predictive value, with scores above 3 indicating an increased perioperative risk and higher scores correlating with more severe OSA; despite varying item importance, its simplicity makes it an efficient and reliable screening tool [25,52,53,54,55].

4.3. Craniofacial Factors and OSA Risk

Cephalometric studies have shown that craniofacial anomalies such as micrognathia, mandibular retraction, and maxillary hypoplasia contribute to upper airway obstruction in OSA, particularly among non-obese individuals [56,57,58,59,60,61]. In this study, the OSA risk increased progressively from class I to class III of dento-maxillary anomalies, with a tendency toward greater daytime sleepiness in classes II and III, although without statistical significance. Class II malocclusions, which are associated with mandibular retraction, are especially linked to a reduced airway size [62,63], while class III anomalies may also compromise airway patency due to a skeletal imbalance [64].
A key strength of this study is its integration of OSA screening tools (STOP-Bang and Epworth) into dental practice. Dentists, who routinely evaluate craniofacial structures, are well positioned to detect the early anatomical signs of OSA—such as retrognathia or an increased neck circumference—thus supporting an interdisciplinary approach that may facilitate the early identification and referral of undiagnosed cases.

4.4. Study Limitations

This study had several limitations. The main constraint was the lack of objective diagnostic confirmation, as most participants were assessed using only self-reported tools (STOP-Bang and Epworth), without polysomnographic validation. Although these questionnaires are useful for identifying individuals at risk, they cannot replace a gold-standard sleep study. The study sample, composed only of adults over 40 from northeastern Romania, also limits the generalizability of the findings. Lastly, the cross-sectional design does not allow for causal conclusions, underlining the need for longitudinal research to assess risk progression over time.

4.5. Future Directions

Future research should aim to validate the STOP-Bang and Epworth screening tools by comparing their results with polysomnographic data in Romanian populations to determine their diagnostic accuracy. It is also important to expand the demographic and geographic scope of studies by including younger individuals and participants from other regions, thus improving the generalizability of the findings. Integrating OSA screening into routine dental practice should be explored, taking advantage of dentists’ accessibility for early detection. Additionally, longitudinal studies are needed to monitor the outcomes of various interventions, such as a specialist referral, CPAP therapy, and weight management, in individuals identified as high-risk. Finally, further investigation into the role of craniofacial structures in OSA, using a cephalometric analysis, may offer deeper insights into the anatomical contributions to the disorder.

5. Conclusions

This study demonstrates that both the STOP-Bang questionnaire and the Epworth sleepiness scale are effective tools for screening obstructive sleep apnea (OSA). STOP-Bang showed a strong predictive value by incorporating clinical and demographic risk factors such as BMI, age, and neck circumference, while Epworth effectively assessed the presence and impact of daytime sleepiness. Their simplicity, accessibility, and ease of administration make them suitable for use in dental settings, where they can support the early identification of individuals at risk for OSA and facilitate timely referral for further evaluation.

Author Contributions

Conceptualization, O.T. and I.Z.; methodology, O.T.; software, C.B. and A.O.A.; validation, O.T. and C.B.; formal analysis, A.O.A.; investigation, A.A. and C.T.; resources, I.Z.; data curation, T.P.; writing—original draft preparation, O.T.; writing—review and editing, A.O.A.; visualization, I.Z.; supervision, T.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the University of Medicine and Pharmacy “Grigore T. Popa” Iasi, Romania, No. 188/25.05.2022. From an ethical point of view, this study was carried out in accordance with Research Law No. 206 of 27 May 2004 on good conduct in scientific research, technological development and innovation, the Integrity Guide in Scientific Research (published by the National Council for Ethics in Scientific Research, Technological Development and Innovation on 12 November 2020), and current European legislation.

Informed Consent Statement

Informed consent was obtained from all the subjects involved in this study.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Abramson, Z.; Susarla, S.M.; Lawler, M.; Bouchard, C.; Troulis, M.; Kaban, L.B. Three-dimensional computed tomographic airway analysis of patients with obstructive sleep apnea treated by maxillomandibular advancement. J. Oral. Maxillofac. Surg. 2011, 69, 677–686. [Google Scholar] [CrossRef]
  2. Schwartz, A.R.; Patil, S.P.; Laffan, A.M.; Polotsky, V.; Schneider, H.; Smith, P.L. Obesity and obstructive sleep apnea: Pathogenic mechanisms and therapeutic approaches. Proc. Am. Thorac. Soc. 2008, 5, 185–192. [Google Scholar] [CrossRef]
  3. Foster, G.D.; Borradaile, K.E.; Sanders, M.H.; Millman, R.; Zammit, G.; Newman, A.B.; Wadden, T.A.; Kelley, D.; Wing, R.R.; Pi-Sunyer, F.X.; et al. A randomized study on the effect of weight loss on obstructive sleep apnea among obese patients with type 2 diabetes: The Sleep AHEAD study. Arch. Intern. Med. 2009, 169, 1619–1626. [Google Scholar] [CrossRef]
  4. Jehan, S.; Zizi, F.; Pandi-Perumal, S.R.; Wall, S.; Auguste, E.; Myers, A.K.; Jean-Louis, G.; McFarlane, S.I. Obstructive Sleep Apnea and Obesity: Implications for Public Health. Sleep Med. Disord. 2017, 1, 00019. [Google Scholar] [PubMed]
  5. Bonsignore, M.R.; McNicholas, W.T.; Montserrat, J.M.; Eckel, J. Adipose tissue in obesity and obstructive sleep apnoea. Eur. Respir. J. 2012, 39, 746–767. [Google Scholar] [CrossRef]
  6. Somers, V.K.; Dyken, M.E.; Clary, M.P.; Abboud, F.M. Sympathetic neural mechanisms in obstructive sleep apnea. J. Clin. Investig. 1995, 96, 1897–1904. [Google Scholar] [CrossRef] [PubMed]
  7. Somers, V.K.; Mark, A.L.; Zavala, D.C.; Abboud, F.M. Contrasting effects of hypoxia and hypercapnia on ventilation and sympathetic activity in humans. J. Appl. Physiol. 1989, 67, 2101–2106. [Google Scholar] [CrossRef] [PubMed]
  8. Kapur, V.K. Obstructive sleep apnea: Diagnosis, epidemiology, and economics. Respir. Care 2010, 55, 1155–1167. [Google Scholar]
  9. Hla, K.M.; Young, T.B.; Bidwell, T.; Palta, M.; Skatrud, J.B.; Dempsey, J. Sleep apnea and hypertension. A population-based study. Ann. Intern. Med. 1994, 120, 382–388. [Google Scholar] [CrossRef]
  10. Nieto, F.J.; Young, T.B.; Lind, B.K.; Shahar, E.; Samet, J.M.; Redline, S.; D’Agostino, R.B.; Newman, A.B.; Lebowitz, M.D.; Pickering, T.G. Association of sleep-disordered breathing, sleep apnea, and hypertension in a large community-based study. Sleep Heart Health Study. JAMA 2000, 283, 1829–1836. [Google Scholar] [CrossRef]
  11. Schäfer, H.; Koehler, U.; Ewig, S.; Hasper, E.; Tasci, S.; Lüderitz, B. Obstructive sleep apnea as a risk marker in coronary artery disease. Cardiology 1999, 92, 79–84. [Google Scholar] [CrossRef] [PubMed]
  12. Marshall, N.S.; Wong, K.K.H.; Liu, P.Y.; Cullen, S.R.J.; Knuiman, M.W.; Grunstein, R.R. Sleep apnea as an independent risk factor for all-cause mortality: The Busselton Health Study. Sleep 2008, 31, 1079–1085. [Google Scholar]
  13. Young, T.; Finn, L.; Peppard, P.E.; Szklo-Coxe, M.; Austin, D.; Nieto, F.J.; Stubbs, B.R.; Hla, K.M. Sleep disordered breathing and mortality: Eighteen-year follow-up of the Wisconsin sleep cohort. Sleep 2008, 31, 1071–1078. [Google Scholar] [PubMed]
  14. Yaggi, H.K.; Concato, J.; Kernan, W.N.; Lichtman, J.H.; Brass, L.M.; Mohsenin, V. Obstructive sleep apnea as a risk factor for stroke and death. N. Engl. J. Med. 2005, 353, 2034–2041. [Google Scholar] [CrossRef] [PubMed]
  15. Punjabi, N.M. The epidemiology of adult obstructive sleep apnea. Proc. Am. Thorac. Soc. 2008, 5, 136–143. [Google Scholar] [CrossRef]
  16. Young, T.; Evans, L.; Finn, L.; Palta, M. Estimation of the clinically diagnosed proportion of sleep apnea syndrome in middle-aged men and women. Sleep 1997, 20, 705–706. [Google Scholar] [CrossRef]
  17. Flemons, W.W.; Whitelaw, W.A.; Brant, R.; Remmers, J.E. Likelihood ratios for a sleep apnea clinical prediction rule. Am. J. Respir. Crit. Care Med. 1994, 150, 1279–1285. [Google Scholar] [CrossRef]
  18. Hoffstein, V.; Szalai, J.P. Predictive value of clinical features in diagnosing obstructive sleep apnea. Sleep 1993, 16, 118–122. [Google Scholar]
  19. Kump, K.; Whalen, C.; Tishler, P.V.; Browner, I.; Ferrette, V.; Strohl, K.P.; Rosenberg, C.; Redline, S. Assessment of the validity and utility of a sleep-symptom questionnaire. Am. J. Respir. Crit. Care Med. 1994, 150, 735–741. [Google Scholar] [CrossRef]
  20. Maislin, G.; Pack, A.I.; Kribbs, N.B.; Smith, P.L.; Schwartz, A.R.; Kline, L.R.; Schwab, R.J.; Dinges, D.F. A survey screen for prediction of apnea. Sleep 1995, 18, 158–166. [Google Scholar] [CrossRef]
  21. Viner, S.; Szalai, J.P.; Hoffstein, V. Are history and physical examination a good screening test for sleep apnea? Ann. Intern. Med. 1991, 115, 356–359. [Google Scholar] [CrossRef]
  22. Abrishami, A.; Khajehdehi, A.; Chung, F. A systematic review of screening questionnaires for obstructive sleep apnea. Can. J. Anaesth. 2010, 57, 423–438. [Google Scholar] [CrossRef] [PubMed]
  23. Douglass, A.B.; Bomstein, R.; Nino-Murcia, G.; Keenan, S.; Miles, L.; Zarcone, V.P.; Guilleminault, C.; Dement, W.C. The Sleep Disorders Questionnaire. I: Creation and multivariate structure of SDQ. Sleep 1994, 17, 160–167. [Google Scholar] [CrossRef]
  24. Netzer, N.C.; Stoohs, R.A.; Netzer, C.M.; Clark, K.; Strohl, K.P. Using the Berlin Questionnaire to identify patients at risk for the sleep apnea syndrome. Ann. Intern. Med. 1999, 131, 485–491. [Google Scholar] [CrossRef]
  25. Chung, F.; Yegneswaran, B.; Liao, P.; Chung, S.A.; Vairavanathan, S.; Islam, S.; Khajehdehi, A.; Shapiro, C.M. STOP questionnaire: A tool to screen patients for obstructive sleep apnea. Anesthesiology 2008, 108, 812–821. [Google Scholar] [CrossRef]
  26. Chung, F.; Subramanyam, R.; Liao, P.; Sasaki, E.; Shapiro, C.; Sun, Y. High STOP-Bang score indicates a high probability of obstructive sleep apnoea. Br. J. Anaesth. 2012, 108, 768–775. [Google Scholar] [CrossRef] [PubMed]
  27. Farney, R.J.; Walker, B.S.; Farney, R.M.; Snow, G.L.; Walker, J.M. The STOP-Bang equivalent model and prediction of severity of obstructive sleep apnea: Relation to polysomnographic measurements of the apnea/hypopnea index. J. Clin. Sleep Med. 2011, 7, 459–465. [Google Scholar] [CrossRef]
  28. Nunes, F.S.; Danzi-Soares, N.J.; Genta, P.R.; Drager, L.F.; Cesar, L.A.; Lorenzi-Filho, G. Critical evaluation of screening questionnaires for obstructive sleep apnea in patients undergoing coronary artery bypass grafting and abdominal surgery. Sleep Breath. 2015, 19, 115–122. [Google Scholar] [CrossRef] [PubMed]
  29. Silva, G.E.; Vana, K.D.; Goodwin, J.L.; Sherrill, D.L.; Quan, S.F. Identification of patients with sleep disordered breathing: Comparing the four-variable screening tool, STOP, STOP-Bang, and Epworth sleepiness scales. J. Clin. Sleep Med. 2011, 7, 467–472. [Google Scholar] [CrossRef]
  30. Oshita, H.; Fuchita, H.; Ito, N.; Senoo, M.; Isoyama, S.; Yamamoto, Y.; Yoshida, A.; Osaki, K.; Kawasaki, K.; Okusaki, K. Validation of the Japanese version of the STOP-Bang test for the risk assessment of obstructive sleep apnea syndrome. J. Jpn. Prim. Care Assoc. 2019, 42, 26–31. [Google Scholar] [CrossRef]
  31. Verwimp, J.; Ameye, L.; Bruyneel, M. Correlation between sleep parameters, physical activity and quality of life in somnolent moderate to severe obstructive sleep apnea adult patients. Sleep Breath. 2013, 17, 1039–1046. [Google Scholar] [CrossRef] [PubMed]
  32. Vana, K.D.; Silva, G.E.; Goldberg, R. Predictive abilities of the STOP-Bang and Epworth Sleepiness Scale in identifying sleep clinic patients at risk for obstructive sleep apnea. Res. Nurs. Health 2013, 36, 84–94. [Google Scholar] [CrossRef]
  33. Kim, H.C.; Kim, J.Y.; Lee, S.H.; Kim, D.K. Predictors for Presence and Severity of Obstructive Sleep Apnea in Snoring Patients. J. Sleep Med. 2015, 12, 47–52. [Google Scholar] [CrossRef]
  34. Johns, M.W. A new method for measuring daytime sleepiness: The Epworth sleepiness scale. Sleep 1991, 14, 540–545. [Google Scholar] [CrossRef] [PubMed]
  35. Deng, X.; Gu, W.; Li, Y.; Liu, M.; Li, Y.; Gao, X. Age-group-specific associations between the severity of obstructive sleep apnea and relevant risk factors in male and female patients. PLoS ONE 2014, 11, 9. [Google Scholar] [CrossRef]
  36. Westreich, R.; Gozlan-Talmor, A.; Geva-Robinson, S.; Schlaeffer-Yosef, T.; Slutsky, T.; Chen-Hendel, E.; Braiman, D.; Sherf, Y.; Arotsker, N.; Abu-Fraiha, Y.; et al. The Presence of Snoring as Well as its Intensity Is Underreported by Women. J. Clin. Sleep Med. 2019, 15, 471–476. [Google Scholar] [CrossRef]
  37. Gharib, A.; Loza, S. Factors affecting the severity of the apnea hypoapnea index: A retrospective study on 838 Egyptian patients diagnosed with obstructive sleep apnea. Egypt. J. Bronchol. 2020, 14, 34. [Google Scholar] [CrossRef]
  38. Tkacova, R.; McNicholas, W.T.; Javorsky, M.; Fietze, I.; Sliwinski, P.; Parati, G.; Grote, L.; Hedner, J. Nocturnal intermittent hypoxia predicts prevalent hypertension in the European sleep Apnoea database cohort study. Eur. Respir. J. 2014, 44, 931–941. [Google Scholar] [CrossRef]
  39. Tanigawa, T.; Tachibana, N.; Yamagishi, K.; Muraki, I.; Kudo, M.; Ohira, T.; Kitamura, A.; Sato, S.; Shimamoto, T.; Iso, H. Relationship between sleep-disordered breathing and blood pressure levels in community-based samples of Japanese men. Hypertens. Res. 2004, 27, 479–484. [Google Scholar] [CrossRef]
  40. Appleton, S.L.; Gill, T.K.; Lang, C.J.; Taylor, A.W.; McEvoy, R.D.; Stocks, N.P.; González-Chica, D.A.; Adams, R.J. Prevalence and comorbidity of sleep conditions in Australian adults: 2016 sleep Health Foundation national survey. Sleep Health 2018, 4, 13–19. [Google Scholar] [CrossRef]
  41. Zhou, X.; Zhou, B.; Li, Z.; Lu, Q.; Li, S.; Pu, Z.; Luo, F. Gender differences of clinical and polysomnographic findings with obstructive sleep apnea syndrome. Sci. Rep. 2021, 11, 5938. [Google Scholar] [CrossRef]
  42. Bixler, E.O.; Vgontzas, A.N.; Lin, H.M.; Have, T.T.; Rein, J.; Vela-Bueno, A.; Kales, A. Prevalence of sleep-disordered breathing in women: Effects of gender. Am. J. Respir. Crit. Care Med. 2001, 163, 608–613. [Google Scholar] [CrossRef] [PubMed]
  43. Redline, S.; Kump, K.; Tishler, P.V.; Browner, I.; Ferrette, V. Gender differences in sleep disordered breathing in a community-based sample. Am. J. Respir. Crit. Care Med. 1994, 149, 722–726. [Google Scholar] [CrossRef] [PubMed]
  44. Perger, E.; Mattaliano, P.; Lombardi, C. Menopause and sleep apnea. Maturitas 2019, 124, 35–38. [Google Scholar] [CrossRef] [PubMed]
  45. Botnaru, V.; Corlateanu, A.; Moldovanu, I. Sindromul de Apnee Obstructivă în Somn: Protocol Clinic Naţional PCN-276; Ministerul Sănătății al Republicii Moldova: Chișinău, Moldova, 2017.
  46. Asavasupreechar, T.; Saito, R.; Edwards, D.P.; Sasano, H.; Boonyaratanakornkit, V. Progesterone receptor isoform B expression in pulmonary neuroendocrine cells decreases cell proliferation. J. Steroid Biochem. 2019, 190, 212–223. [Google Scholar] [CrossRef]
  47. Thompson, C.; Legault, J. A portrait of obstructive sleep apnea risk factors in middle-aged and older adults in the Canadian Longitudinal Study on Aging. Sci. Rep. 2022, 12, 5127. [Google Scholar] [CrossRef]
  48. Siwasaranond, N.; Nimitphong, H.; Manodpitipong, A.; Saetung, S.; Chirakalwasan, N.; Thakkinstian, A.; Reutrakul, S. The Relationship between Diabetes-Related Complications and Obstructive Sleep Apnea in Type 2 Diabetes. J. Diabetes Res. 2018, 2018, 9269170. [Google Scholar] [CrossRef]
  49. Mokhlesi, B.; Ham, S.A.; Gozal, D. The effect of sex and age on the comorbidity burden of OSA: An observational analysis from a large nationwide US health claims database. Eur. Respir. J. 2016, 47, 1162–1169. [Google Scholar] [CrossRef]
  50. Senaratna, C.V.; Perret, J.L.; Lodge, C.J.; Lowe, A.J.; Campbell, B.E.; Matheson, M.C.; Hamilton, G.S.; Dharmage, S.C. Prevalence of obstructive sleep apnea in the general population: A systematic review. Sleep Med. Rev. 2017, 34, 70–81. [Google Scholar] [CrossRef]
  51. Engleman, H.M.; Douglas, N.J. Sleepiness, cognitive function, and quality of life in obstructive sleep apnoea/hypopnoea syndrome. Thorax 2004, 59, 618–622. [Google Scholar] [CrossRef]
  52. Chiu, H.Y.; Chen, P.Y.; Chuang, L.P.; Chen, N.H.; Tu, Y.K.; Hsieh, Y.J.; Wang, Y.C.; Guilleminault, C. Diagnostic accuracy of the Berlin questionnaire, STOP-BANG, STOP, and Epworth sleepiness scale in detecting obstructive sleep apnea: A bivariate meta-analysis. Sleep Med. Rev. 2017, 36, 57–70. [Google Scholar] [CrossRef] [PubMed]
  53. Kimoff, R.S. Obstructive Sleep Apnea. In International Encyclopedia of Public Health, 6th ed.; Elsevier Inc.: Amsterdam, The Netherlands, 2016; pp. 308–314. [Google Scholar]
  54. Chan, A.S.; Phillips, C.L.; Cistulli, P.A. Obstructive sleep apnoea—An update. Intern. Med. J. 2010, 40, 102–106. [Google Scholar] [CrossRef]
  55. Marshall, N.S.; Wong, K.K.; Phillips, C.L.; Liu, P.Y.; Knuiman, M.W.; Grunstein, R.R. Is sleep apnea an independent risk factor for prevalent and incident diabetes in the Busselton Health Study? J. Clin. Sleep Med. 2009, 5, 15–20. [Google Scholar] [CrossRef] [PubMed]
  56. Vasu, T.S.; Grewal, R.; Doghramji, K. Obstructive sleep apnea syndrome and perioperative complications: A systematic review of the literature. J. Clin. Sleep Med. 2012, 8, 199–207. [Google Scholar] [CrossRef] [PubMed]
  57. Vasu, T.S.; Doghramji, K.; Cavallazzi, R.; Grewal, R.; Hirani, A.; Leiby, B.; Markov, D.; Reiter, D.; Kraft, W.K.; Witkowski, T. Obstructive Sleep Apnea Syndrome and Postoperative Complications: Clinical Use of the STOP-Bang Questionnaire. Sleep Med. 2012, 13, 929–934. [Google Scholar] [CrossRef]
  58. Pack, A.I.; Gislason, T. Obstructive sleep apnea and cardiovascular disease: A perspective and future directions. Prog. Cardiovasc. Dis. 2009, 51, 434–451. [Google Scholar] [CrossRef]
  59. Bearpark, H.; Elliott, L.; Grunstein, R.; Cullen, S.; Schneider, H.; Althaus, W.; Sullivan, C. Snoring and sleep apnea. A population study in Australian men. Am. J. Respir. Crit. Care Med. 1995, 151, 1459–1465. [Google Scholar] [CrossRef]
  60. Ip, M.S.; Lam, B.; Tang, L.C.; Lauder, I.J.; Ip, T.Y.; Lam, W.K. A community study of sleep-disordered breathing in middle-aged Chinese women in Hong Kong: Prevalence and gender differences. Chest 2004, 125, 127–134. [Google Scholar] [CrossRef]
  61. Pirelli, P.; Saponara, M.; Guilleminault, C. Rapid maxillary expansion before and after adenotonsillectomy in children with obstructive sleep apnea. Somnologie 2012, 16, 125–132. [Google Scholar] [CrossRef]
  62. Abdolreza, J.; Saeed, K.; Alireza, D. Treatment Protocol for Skeletal Class III Malocclusion in Growing Patients. In Motamedi MH. A Textbook of Advanced Oral and Maxillofacial Surgery; IntechOpen: London, UK, 2016; Volume 3. [Google Scholar]
  63. Schwab, R.J.; Pasirstein, M.; Pierson, R.; Mackley, A.; Hachadoorian, R.; Arens, R.; Maislin, G.; Pack, A.I. Identification of upper air-way anatomic risk factors for obstructive sleep apnea with volumetric magnetic resonance imaging. Am. J. Respir. Crit. Care Med. 2003, 168, 522–530. [Google Scholar] [CrossRef]
  64. Neelapu, B.C.; Kharbanda, O.P.; Sardana, H.K.; Balachandran, R.; Sardana, V.; Kapoor, P.; Gupta, A.; Vasamsetti, S. Craniofacial and upper airway morphology in adult obstructive sleep apnea patients: A systematic review and meta-analysis of cephalometric studies. Sleep Med. Rev. 2017, 31, 79–90. [Google Scholar] [CrossRef] [PubMed]
Dentistry 13 00208 g001
Figure 1. Distribution of subjects according to the risk calculated using the STOP-Bang questionnaire and the class of dento-maxillary anomaly.
Figure 1. Distribution of subjects according to the risk calculated using the STOP-Bang questionnaire and the class of dento-maxillary anomaly.
Dentistry 13 00208 g001
Dentistry 13 00208 g002
Figure 2. Distribution of subjects according to risk calculated using the Epworth questionnaire and dento-maxillary anomaly class.
Figure 2. Distribution of subjects according to risk calculated using the Epworth questionnaire and dento-maxillary anomaly class.
Dentistry 13 00208 g002
Table 1. Demographic characteristics of the group.
Table 1. Demographic characteristics of the group.
No%
Age61.25 ± 7.165 Years (Minimum Age: 42, Maximum Age: 75)
Gender
Female9141.0
Male13159.0
Residence
Urban15168.0
Rural7132.0
Socio-economic level
High socio-economic level12556.3
Medium socio-economic level6830.6
Low socio-economic level2913.1
Distribution of participants diagnosed with OSA
No19085.6
Yes3214.4
Distribution of participants according to BMI
Normal weight (<25 kg/m2)1.5
Overweight (25–30 kg/m2)7533.8
Obese (>30 kg/m2)14665.8
Distribution of participants according to associated pathologies
No156.8
Yes20793.2
Table 2. Distribution of participants’ responses to the STOP-Bang questionnaire according to gender, BMI, and associated pathologies.
Table 2. Distribution of participants’ responses to the STOP-Bang questionnaire according to gender, BMI, and associated pathologies.
GenderBMIAssociated Pathologies
FemaleMaleNormal Weight Increased WeightObeseNoYes
You snore loudlyNo33.0%22.9%100.0%46.7%16.4%100.0%21.7%
Yes67.0%77.1% 53.3%83.6% 78.3%
p0.0970.0000.000
You are tired, exhausted, or sleepy during the dayNo31.9%38.9% 74.7%16.4%80.0%32.9%
Yes68.1%61.1%100.0%25.3%83.6%20.0%67.1%
p0.2810.0000.000
You have sleep apneaNo49.5%45.0% 74.7%32.9%80.0%44.4%
Yes50.5%55.0%100.0%25.3%67.1%20.0%55.6%
p0.5170.0000.008
You have high blood pressureNo60.4%49.6%100.0%60.0%50.7%86.7%51.7%
Yes39.6%50.4% 40.0%49.3%13.3%48.3%
p0.1120.2750.009
You are over 50 years oldNo2.2%8.4%100.0%16.0% 86.7%
Yes97.8%91.6% 84.0%100.0%13.3%100.0%
p0.0530.0000.000
Circumference <40 cm52.7%64.9%100.0%86.7%45.9%100.0%57.0%
Circumference >41 cm47.3%35.1% 13.3%54.1% 43.0%
p0.0700.0000.001
Low risk of obstructive sleep apnea: “Yes” to 0–2 questions15.4%16.8%100.0%46.7% 86.7%11.1%
Medium risk of obstructive sleep apnea: “Yes” to 3–4 questions27.5%32.8% 41.3%25.3% 32.9%
Increased risk of obstructive sleep apnea: “Yes” to 5–8 questions57.1%50.4% 12.0%74.7%13.3%56.0%
p0.5980.0000.001
Table 3. Estimation of the chances of developing sleep apnea according to age, BMI, and classification into a risk group using the STOP-Bang questionnaire.
Table 3. Estimation of the chances of developing sleep apnea according to age, BMI, and classification into a risk group using the STOP-Bang questionnaire.
RISKBStd. ErrorWaldSig.Exp(B)95% Confidence Interval for Exp(B)
Lower BoundUpper Bound
Low risk of obstructive sleep apneaIntercept−1.3600.20842.6900.000
[age2 = 1.00]1.6970.6217.4530.0065.4551.61418.441
Medium risk of obstructive sleep apneaIntercept−0.6330.16015.7050.000
[age2 = 1.00]1.1030.5923.4710.0623.0130.9449.616
Medium risk of obstructive sleep apneaIntercept−1.0800.19032.2450.000
[Body mass index = 1]0.5290.000 1.6981.6981.698
[Body mass index = 2]2.3170.42429.9010.00010.1474.42223.283
Table 4. Distribution of participants’ responses to the Epworth questionnaire according to gender, BMI, and associated pathologies.
Table 4. Distribution of participants’ responses to the Epworth questionnaire according to gender, BMI, and associated pathologies.
GenderIMCAssociated Pathologies
FemaleMaleNormal WeightIncreased WeightObese > 30 kg/m2NoYes
Sitting and reading a book or a newspaper
Never doze/fall asleep2.2%8.4%100.0%16.0% 86.7%
Low probability of dozing/falling asleep13.2%24.4% 58.7% 21.3%
Moderate likelihood of dozing/falling asleep62.6%38.2% 25.3%60.3% 51.7%
High probability of dozing/falling asleep22.0%29.0% 39.7%13.3%27.1%
p0.0020.0000.000
Watching a show on TV
Never doze/fall asleep2.2%8.4%100.0%16.0% 86.7%
Low probability of dozing/falling asleep 16.0% 28.0% 10.1%
Moderate likelihood of dozing/falling asleep48.4%38.9% 56.0%36.3% 45.9%
High probability of dozing/falling asleep49.5%36.6% 63.7%13.3%44.0%
p0.0000.0000.000
Sitting, inactive, in a public place (cinema, theatre, waiting room)
Never doze/fall asleep2.2%24.4%100.0%44.0% 86.7%10.1%
Low probability of dozing/falling asleep2.2%16.0% 25.3%2.7% 11.1%
Moderate likelihood of dozing/falling asleep78.0%46.6% 30.7%74.7%13.3%62.8%
High probability of dozing/falling asleep17.6%13.0% 22.6% 15.9%
p0.0000.0000.000
Passenger in a vehicle for a trip of at least 1 h
Never doze/fall asleep1.1%8.4%100.0%14.7% 80.0%
Low probability of dozing/falling asleep3.3%32.1% 54.7%2.7%6.7%21.3%
Moderate likelihood of dozing/falling asleep35.2%24.4% 43.8%13.3%30.0%
High probability of dozing/falling asleep60.4%35.1% 30.7%53.4% 48.8%
p0.0000.000.000
Sitting in bed, after lunch
Never doze/fall asleep2.2%8.4%100.0%16.0% 86.7%
Low probability of dozing/falling asleep13.2%24.4% 58.7% 21.3%
Moderate likelihood of dozing/falling asleep62.6%38.2% 25.3%60.3% 51.7%
High probability of dozing/falling asleep22.0%29.0% 39.7%13.3%27.1%
p0.0020.0000.000
Sitting and talking to someone
Never doze/fall asleep56.0%58.8% 90.7%41.1%80.0%56.0%
Low probability of dozing/falling asleep41.8%31.3%100.0%9.3%48.6%20.0%36.7%
Moderate likelihood of dozing/falling asleep2.2%9.9% 10.3% 7.2%
High probability of dozing/falling asleep56.0%58.8% 90.7%41.1%80.0%56.0%
p0.0390.0000.167
Seated, after a meal without alcoholic beverages
Never doze/fall asleep31.9%32.1%100.0%58.7%17.8%93.3%27.5%
Low probability of dozing/falling asleep30.8%32.8% 41.3%27.4%6.7%33.8%
Moderate likelihood of dozing/falling asleep37.4%35.1% 54.8% 38.6%
High probability of dozing/falling asleep31.9%32.1%100.0%58.7%17.8%93.3%27.5%
p0.9290.0000.000
Driving a car during a traffic stop lasting a few minutes
Never doze/fall asleep33.0%41.2%100.0%72.0%19.9%100.0%33.3%
Low probability of dozing/falling asleep45.1%35.9% 28.0%45.9% 42.5%
Moderate likelihood of dozing/falling asleep22.0%22.9% 34.2% 24.2%
p0.3450.0000.000
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Tanellari, O.; Alushi, A.; Toti, C.; Balcos, C.; Armencia, A.O.; Panaite, T.; Zetu, I. Screening for Obstructive Sleep Apnea Among the Adult Population in the Northeastern Region of Romania. Dent. J. 2025, 13, 208. https://doi.org/10.3390/dj13050208

AMA Style

Tanellari O, Alushi A, Toti C, Balcos C, Armencia AO, Panaite T, Zetu I. Screening for Obstructive Sleep Apnea Among the Adult Population in the Northeastern Region of Romania. Dentistry Journal. 2025; 13(5):208. https://doi.org/10.3390/dj13050208

Chicago/Turabian Style

Tanellari, Olja, Adela Alushi, Celiana Toti, Carina Balcos, Adina Oana Armencia, Tinela Panaite, and Irina Zetu. 2025. "Screening for Obstructive Sleep Apnea Among the Adult Population in the Northeastern Region of Romania" Dentistry Journal 13, no. 5: 208. https://doi.org/10.3390/dj13050208

APA Style

Tanellari, O., Alushi, A., Toti, C., Balcos, C., Armencia, A. O., Panaite, T., & Zetu, I. (2025). Screening for Obstructive Sleep Apnea Among the Adult Population in the Northeastern Region of Romania. Dentistry Journal, 13(5), 208. https://doi.org/10.3390/dj13050208

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop