Am J Epidemiol 177(9): 1006-1014

PMC: PMC3639722

PMID: 23589584

doi: 10.1093/aje/kws342

Increased Prevalence of Sleep-Disordered Breathing in Adults

^{Correspondence to Dr. Paul E. Peppard, Department of Population Health Sciences, University of Wisconsin–Madison, WARF Building 685, 610 Walnut St., Madison, WI 53726 (e-mail:}ude.csiw@drappepp).

Contributed by

Abbreviations: AHI, apnea-hypopnea index; BMI, body mass index; NHANES, National Health and Nutrition Examination Survey; SDB, sleep-disordered breathing.

Received 2012 May 11; Accepted 2012 Aug 7.

Copyright © The Author 2013. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Abstract

Sleep-disordered breathing is a common disorder with a range of harmful sequelae. Obesity is a strong causal factor for sleep-disordered breathing, and because of the ongoing obesity epidemic, previous estimates of sleep-disordered breathing prevalence require updating. We estimated the prevalence of sleep-disordered breathing in the United States for the periods of 1988–1994 and 2007–2010 using data from the Wisconsin Sleep Cohort Study, an ongoing community-based study that was established in 1988 with participants randomly selected from an employed population of Wisconsin adults. A total of 1,520 participants who were 30–70 years of age had baseline polysomnography studies to assess the presence of sleep-disordered breathing. Participants were invited for repeat studies at 4-year intervals. The prevalence of sleep-disordered breathing was modeled as a function of age, sex, and body mass index, and estimates were extrapolated to US body mass index distributions estimated using data from the National Health and Nutrition Examination Survey. The current prevalence estimates of moderate to severe sleep-disordered breathing (apnea-hypopnea index, measured as events/hour, ≥15) are 10% (95% confidence interval (CI): 7, 12) among 30–49-year-old men; 17% (95% CI: 15, 21) among 50–70-year-old men; 3% (95% CI: 2, 4) among 30–49-year-old women; and 9% (95% CI: 7, 11) among 50–70 year-old women. These estimated prevalence rates represent substantial increases over the last 2 decades (relative increases of between 14% and 55% depending on the subgroup).

Keywords: adult, middle age, obesity, sleep

Abstract

The apnea and hypopnea events of sleep-disordered breathing (SDB) have substantial harmful health consequences. Immediate effects include intermittent hypoxia, fragmented sleep, and exaggerated fluctuations in heart rhythm, blood pressure, and intrathoracic pressure (1). In turn, these acute physiologic disruptions evolve into long-term sequelae, such as hypertension and cardiovascular morbidities (1–3), decrements in cognitive function (4, 5), mood and quality of life (6, 7), and premature death (8, 9).

In 1993, data collected over a 4-year period from the Wisconsin Sleep Cohort Study uncovered a high prevalence of SDB assessed using polysomnography in a working population–based sample of adults 30–60 years of age (10). The findings were corroborated by other US population-based studies (11), but these prevalence rates were estimated more than a decade ago (12, 13). The most important modifiable causes of SDB in adult populations are overweight and obesity. Weight gain and loss have been consistently associated with increasing and decreasing SDB severity, respectively, in observational and intervention studies (14–16). Over the last few decades, the prevalence rates of overweight and obesity experienced epidemic trajectories in the United States (17–20), which is likely to have resulted in increased occurrence of obesity-related outcomes, including SDB.

In the United States, a systematic program for monitoring SDB prevalence over time does not exist. High-quality objective assessments of SDB are time-consuming, expensive, and burdensome to subjects, typically requiring overnight continuous monitoring of multiple physiologic processes, including sleep/wake state and respiratory functioning. As a consequence, despite the high prevalence of and broad range of negative health outcomes from SDB, there is presently no national monitoring system for tracking SDB prevalence in a fashion analogous to the manner in which iterations of the US National Health and Nutrition Examination Survey (NHANES) are used to track the prevalence rates of health exposures and outcomes such as overweight and obesity, hypertension, and blood lead levels. However, if a robust SDB prevalence estimation model is developed that has as input parameters factors that are routinely tracked and accurately estimated, that model could allow for serial estimations of SDB prevalence.

In the present study, we developed models of SDB prevalence as a function of sex, age group, and weight status categories, the 3 most important factors in SDB prevalence in US populations (11). We applied the models to adult (30–70 years of age) SDB prevalence in the early 1990s and in a recent time period (2007–2010), framing a time interval that corresponded with a rapid expansion of the US obesity epidemic. This was performed by combining information from the Wisconsin Sleep Cohort Study, which since 1988 has performed more than 4,500 overnight in-laboratory SDB evaluations on 1,520 study participants, and the NHANES in an approach that increased our new estimates’ generalizability and replaced previous, now-outdated prevalence estimates (10, 16). We did this in 3 steps: 1) using data from participants in the ongoing Wisconsin Sleep Cohort Study, we modeled sex, age, and body mass index (BMI) strata–specific prevalence estimates of SDB; 2) we used NHANES (21) data to estimate US population distributions of corresponding sex, age, and BMI strata for 2 periods, the early 1990s (representing the initiation of the Wisconsin Sleep Cohort) and the late 2000s; and 3) we applied the Wisconsin Sleep Cohort SDB prevalence estimates to the 2 periods. This process yielded estimates of US adult SDB prevalence in the early 1990s and late 2000s.

Abbreviation: ESS, Epworth Sleepiness Scale.

^{For sex, the value is the percentage of participants (}n = 1,520); for age, body mass index, and apnea-hypopnea index, the value is the percentage of polysomnography studies (n = 4,563; individual participants contributed multiple observations, potentially ranging across age, body mass index, and sleep-disordered breathing categories).

^{Weight (kg)/height (m)}^.

^Events/hour.

^{The ESS was added to Wisconsin Sleep Cohort Study protocols in 1993 and was available from 3,623 polysomnography studies by 1,291 participants.}

Abbreviations: AHI, apnea-hypopnea index; CI, confidence interval; SE, standard error.

^{Estimated logistic regression model coefficients: intercept = −10.3 (SE, 1.3); β}_Sex = −1.6 (SE, 0.2; female = 1, male = 0); β_Age = 2.1 (SE, 0.5; 30–49 years of age = 0, 50–70 years of age = 1); β_BMI = 0.41 (SE, 0.07; <25 = 23.0, 25–29.9 = 27.6, 30–39.9 = 33.9, ≥40 = 45.4); β_BMI2 = −0.003 (SE, 0.001); β_Age×BMI = −0.041 (SE, 0.015); and β_Age×Sex = 0.82 (SE, 0.22).

^{Weight (kg)/height (m)}^.

^Events/hour.

^{Among 1,520 participants who contributed a total of 4,563 sleep studies.}

Abbreviations: AHI, apnea-hypopnea index; CI, confidence interval; SE, standard error.

^{Estimated logistic regression model coefficients: intercept = −15.2 (SE, 1.8); β}_Sex = −1.7 (SE, 0.3; female = 1, male = 0); β_Age = 2.7 (SE, 0.7; 0–49 years of age = 0, 50–70 years of age = 1); β_BMI = 0.58 (SE, 0.10; <25 = 23.0, 25–29.9 = 27.6, 30–39.9 = 33.9, ≥40 = 45.4); β_BMI2 = −0.005 (SE, 0.001); β_Age×BMI = −0.059 (SE, 0.021); and β_Age×Sex = 0.75 (SE, 0.33).

^{Weight (kg)/height (m)}^.

^Events/hour.

^{Among 1,520 participants who contributed a total of 4,563 sleep studies.}

Abbreviations: AHI, apnea-hypopnea index; CI, confidence interval; ESS, Epworth Sleepiness Scale; SE, standard error.

^{Estimated logistic regression model coefficients: intercept = −9.9 (SE, 1.6); β}_Sex = −1.7 (SE, 0.3; female = 1, male = 0); β_Age = 2.6 (SE, 0.7; 0–49 years of age = 0, 50–70 years of age = 1); β_BMI = 0.33 (SE, 0.09; <25 = 23.0, 25–29.9 = 27.6, 30–39.9 = 33.9, ≥40 = 45.4); β_BMI2 = −0.002 (SE, 0.001); β_Age×BMI = −0.07 (SE, 0.02); and β_Age×Sex = 0.70 (SE, 0.33).

^{Weight (kg)/height (m)}^.

^Events/hour.

^{Among 1,291 participants who contributed a total of 3,623 sleep studies.}

Abbreviations: AHI, apnea-hypopnea index; CI, confidence interval; ESS, Epworth Sleepiness Scale; SE, standard error.

^{Estimated logistic regression model coefficients: intercept = −15.9 (SE, 3.1); β}_Sex = −2.2 (SE, 0.5; female = 1, male = 0); β_Age = 2.7 (SE, 1.1; 0–49 years of age = 0, 50–70 years of age = 1); β_BMI = 0.57 (SE, 0.17; <25 = 23.0, 25–29.9 = 27.6, 30–39.9 = 33.9, ≥40 = 45.4); β_BMI2 = −0.005 (SE, 0.002); β_Age×BMI = −0.07 (SE, 0.03); β_Age×Sex = 1.2 (SE, 0.5).

^{Weight (kg)/height (m)}^.

^Events/hour.

^{Among 1,520 participants who contributed a total of 4,563 sleep studies.}

Abbreviations: AHI, apnea-hypopnea index; CI, confidence interval; ESS, Epworth Sleepiness Scale.

^Events/hour.

ACKNOWLEDGMENTS

Author affiliations: Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin–Madison, Madison, Wisconsin (Paul E. Peppard, Terry Young, Jodi H. Barnet, Mari Palta, Erika W. Hagen); Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin–Madison, Madison, Wisconsin (Mari Palta); and Department of Medicine, School of Medicine and Public Health, University of Wisconsin–Madison, Madison, Wisconsin (Khin Mae Hla).

This work was supported by the National Heart, Lung, and Blood Institute (grant R01HL62252), National Institute of Aging (grants 1R01AG036838; and R01AG14124), and the National Center for Research Resources (grant 1UL1RR025011) at the National Institutes of Health.

We thank the following people for their valuable assistance: Diane Austin, Laurel Finn, Amanda Rasmuson, Kathryn Pluff, Robin Stubbs, Nicole Salzieder, Kathy Stanback, Mary Sundstrom, Dr. Kathryn M. Cacic, Dr. Steven Weber, and Dr. Steven R. Barczi.

Conflict of interest: none declared.

ACKNOWLEDGMENTS

REFERENCES

References

1. Somers VK, White DP, Amin R, et al Sleep apnea and cardiovascular disease: an American Heart Association/American College of Cardiology Foundation scientific statement from the American Heart Association Council for High Blood Pressure Research Professional Education Committee, Council on Clinical Cardiology, Stroke Council, and Council on Cardiovascular Nursing. J Am Coll Cardiol. 2008;52(8):686–717.[PubMed][Google Scholar]
2. Shamsuzzaman AS, Gersh BJ, Somers VKObstructive sleep apnea: implications for cardiac and vascular disease. JAMA. 2003;290(14):1906–1914.[PubMed][Google Scholar]
3. Peppard PE, Young T, Palta M, et al Prospective study of the association between sleep-disordered breathing and hypertension. N Engl J Med. 2000;342(19):1378–1384.[PubMed][Google Scholar]
4. Yaffe K, Laffan AM, Harrison SL, et al Sleep-disordered breathing, hypoxia, and risk of mild cognitive impairment and dementia in older women. JAMA. 2011;306(6):613–619.[Google Scholar]
5. Kim HC, Young T, Matthews CG, et al. Sleep-disordered breathing and neuropsychological deficits. A population-based study. Am J Respir Crit Care Med. 1997;156(6):1813–1819.[PubMed]
6. Baldwin CM, Griffith KA, Nieto FJ, et al The association of sleep-disordered breathing and sleep symptoms with quality of life in the Sleep Heart Health Study. Sleep. 2001;24(1):96–105.[PubMed][Google Scholar]
7. Peppard PE, Szklo-Coxe M, Hla KM, et al Longitudinal association of sleep-related breathing disorder and depression. Arch Intern Med. 2006;166(16):1709–1715.[PubMed][Google Scholar]
8. Young T, Finn L, Peppard PE, et al Sleep disordered breathing and mortality: eighteen-year follow-up of the Wisconsin sleep cohort. Sleep. 2008;31(8):1071–1078.[Google Scholar]
9. Marshall NS, Wong KK, Liu PY, et al Sleep apnea as an independent risk factor for all-cause mortality: the Busselton Health Study. Sleep. 2008;31(8):1079–1085.[Google Scholar]
10. Young T, Palta M, Dempsey J, et al The occurrence of sleep-disordered breathing among middle-aged adults. N Engl J Med. 1993;328(17):1230–1235.[PubMed][Google Scholar]
11. Young T, Peppard PE, Gottlieb DJEpidemiology of obstructive sleep apnea: a population health perspective. Am J Respir Crit Care Med. 2002;165(9):1217–1239.[PubMed][Google Scholar]
12. Bixler EO, Vgontzas AN, Ten Have T, et al. Effects of age on sleep apnea in men: I. Prevalence and severity. Am J Respir Crit Care Med. 1998;157(1):144–148.[PubMed]
13. Bixler EO, Vgontzas AN, Lin HM, et al Prevalence of sleep-disordered breathing in women: effects of gender. Am J Respir Crit Care Med. 2001;163(3 Pt 1):608–613.[PubMed][Google Scholar]
14. Newman AB, Foster G, Givelber R, et al Progression and regression of sleep-disordered breathing with changes in weight: the Sleep Heart Health Study. Arch Intern Med. 2005;165(20):2408–2413.[PubMed][Google Scholar]
15. Peppard PE, Young T, Palta M, et al Longitudinal study of moderate weight change and sleep-disordered breathing. JAMA. 2000;284(23):3015–3021.[PubMed][Google Scholar]
16. Young T, Peppard PE, Taheri SExcess weight and sleep-disordered breathing. J Appl Physiol. 2005;99(4):1592–1599.[PubMed][Google Scholar]
17. Flegal KM, Carroll MD, Ogden CL, et al Prevalence and trends in obesity among US adults, 1999–2008. JAMA. 2010;303(3):235–241.[PubMed][Google Scholar]
18. Ogden CL, Carroll MD, Curtin LR, et al Prevalence of overweight and obesity in the United States, 1999–2004. JAMA. 2006;295(13):1549–1555.[PubMed][Google Scholar]
19. Hedley AA, Ogden CL, Johnson CL, et al Prevalence of overweight and obesity among US children, adolescents, and adults, 1999–2002. JAMA. 2004;291(23):2847–2850.[PubMed][Google Scholar]
20. Flegal KM, Carroll MD, Ogden CL, et al Prevalence and trends in obesity among US adults, 1999–2000. JAMA. 2002;288(14):1723–1727.[PubMed][Google Scholar]
21. US National Center for Health Statistics. National Health and Nutrition Examination Survey (NHANES) Hyattsville, MD: National Center for Health Statistics; 2012. . (Accessed August 6, 2012) [PubMed]
22. Young T, Palta M, Dempsey J, et al Burden of sleep apnea: Rationale, design, and major findings of the Wisconsin Sleep Cohort Study. Wisconsin Med J. 2009;108(5):246–249.[Google Scholar]
23. Lohman TG, Roche AF, Martorell R, editors. Anthropometric Standardization Reference Manual. Champaign, IL: Human Kinetics; 1988. pp. 1–55. [PubMed]
24. Johns MWA new method for measuring daytime sleepiness: the Epworth Sleepiness Scale. Sleep. 1991;14(6):540–545.[PubMed][Google Scholar]
25. Johns MW. Daytime sleepiness, snoring, and obstructive sleep apnea. The Epworth Sleepiness Scale. Chest. 1993;103(1):30–36.[PubMed]
26. Rechtscahaffen A, Kales A. Washington, DC: US Government Printing Office; 1968. A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects. National Institutes of Health publication no. 204. [[PubMed]
27. Liang K-Y, Zeger SLLongitudinal data analysis using generalized linear models. Biometrika. 1986;73(1):13–22.[PubMed][Google Scholar]
28. US National Center for Health Statistics. National Health and Nutrition Examination Survey (NHANES) III Series 11 Data Files. Hyattsville, MD: National Center for Health Statistics; 2012. . (Accessed August 6, 2012) [PubMed]
29. US National Center for Health Statistics. 2007–2008 National Health and Nutrition Examination Survey (NHANES): Survey Data, Documentation, Codebooks, SAS Code. Hyattsville, MD: National Center for Health Statistics; 2012. . (Accessed August 6, 2012) [PubMed]
30. US National Center for Health Statistics. 2009–2010 National Health and Nutrition Examination Survey (NHANES): Survey Data, Documentation, Codebooks, SAS Code. Hyattsville, MD: National Center for Health Statistics; 2012. . (Accessed August 6, 2012) [PubMed]
31. Rao JNK, Wu CFJResampling inference with complex survey data. J Am Stat Assoc. 1988;83(401):231–241.[PubMed][Google Scholar]
32. Centers for Medicare and Medicaid Services, US Department of Health and Human Services. Decision Memo for Continuous Positive Airway Pressure (CPAP) Therapy for Obstructive Sleep Apnea (OSA) (CAG-00093R2) Baltimore, MD: Centers for Medicare and Medicaid Services; 2008. (Accessed August 6, 2012) [PubMed]
33. Yaggi HK, Concato J, Kernan WN, et al Obstructive sleep apnea as a risk factor for stroke and death. N Engl J Med. 2005;353(19):2034–2041.[PubMed][Google Scholar]
34. Gottlieb DJ, Yenokyan G, Newman AB, et al Prospective study of obstructive sleep apnea and incident coronary heart disease and heart failure: the sleep heart health study. Circulation. 2010;122(4):352–360.[Google Scholar]
35. Schwartz AR, Patil SP, Laffan AM, et al Obesity and obstructive sleep apnea: pathogenic mechanisms and therapeutic approaches. Proc Am Thorac Soc. 2008;5(2):185–192.[Google Scholar]
36. Peppard PE, Ward NR, Morrell MJThe impact of obesity on oxygen desaturation during sleep-disordered breathing. Am J Respir Crit Care Med. 2009;180(8):788–793.[Google Scholar]
37. Wing RR, Phelan SLong-term weight loss maintenance. Am J Clin Nutr. 2005;82(suppl):222S–225S.[PubMed][Google Scholar]
38. Sawyer AM, Gooneratne N, Marcus CL, et al A systematic review of CPAP adherence across age groups: clinical and empiric insights for developing CPAP adherence interventions. Sleep Med Rev. 2011;15(6):343–356.[Google Scholar]
39. Redline S, Tishler PV, Hans MG, et al Racial differences in sleep-disordered breathing in African-Americans and Caucasians. Am J Respir Crit Care Med. 1997;155(1):186–192.[PubMed][Google Scholar]
40. Young T, Shahar E, Nieto F, et al Predictors of sleep-disordered breathing in community-dwelling adults: the Sleep Heart Health Study. Arch Intern Med. 2002;162(8):893–900.[PubMed][Google Scholar]
41. Yamagishi K, Ohira T, Nakano H, et al Cross-cultural comparison of the sleep-disordered breathing prevalence among Americans and Japanese. Eur Respir J. 2010;36(2):379–384.[Google Scholar]