Epidemiology of Obstructive Sleep Apnea: a Population-based Perspective

Abstract

This review summarizes the recent literature on the epidemiology of adult obstructive sleep apnea (OSA) from various population-based studies. Despite methodologic differences, comparisons have yielded similar prevalence rates of the OSA syndrome in various geographic regions and amongst a number of ethnic groups. Risk factors for OSA including obesity, aging, gender, menopause, and ethnicity are analyzed. We also provide discussion on adverse medical conditions associated with OSA including hypertension, stroke, congestive heart failure, coronary artery disease, cardiovascular mortality, insulin resistance, and neurocognitive dysfunction. Finally with the progression of the global obesity epidemic, we focus on the economic health care burden of OSA and the importance of recognizing the largely undiagnosed OSA population with emphasis on strategies to improve access to diagnostic resources.

Figure 1

The prevalence of OSA (defined as either AHI ≥ 10 or 15) in various medical disorders. Compared to three population-based prevalence studies [–23], patients with Type 2 diabetes [68,69], polycystic ovary syndrome [70,72], coronary artery disease [–76], congestive heart failure [–79], and stroke [–82] have a much higher prevalence of OSA. Each bar in the figure represents an individual study.

Figure 2

Incidence of non-fatal and fatal cardiovascular events in 10 years (events/100 person years) in controls, snorers, untreated mild to moderate OSA, untreated severe OSA, and OSA treated with CPAP in an observational study. All participants in this study were men. The healthy men were age and BMI matched to patients with OSA (healthy men had a mean age of 49.6 years and a mean BMI of 29.8 kg/m² vs. men with untreated severe OSA who had a mean age of 49.9 years and a mean of BMI 30 kg/m²). Adherence with CPAP treatment was assessed with the timer built into each CPAP device. Patients with severe OSA had a mean CPAP use of ≥ 4 h/day. Data adapted from Marin et al. [40]. *p < 0.0001 versus healthy men †p < 0.0012 versus healthy men

Figure 3

Kaplan-Meier cumulative survival curve according to long-term CPAP adherence in patients with OSA emphasizing the relationship between average daily adherence with CPAP therapy and mortality. In this study, a historical cohort of 871 patients with moderate to severe OSA (mean age 55.4 ± 10.6 years, 81% men, mean AHI and CPAP use were 55.1 ± 28.7 and 10.1 ± 2.1 cm H₂O respectively) were followed for an average of 49 ± 23 months. There were 322 patients in the group with CPAP adherence > 6 h/day (mean adherence 7.6 ± 1.2 hours), 342 patients in the group with 1–6 hour/day (mean adherence 3.9 ± 1.4 hours) and 85 patients in the group with < 1 h/day (mean adherence 0.3 ± 0.2 hours). Variables that independently correlated with mortality in themultivariate analysis were CPAP adherence, hypertension, and forced expiratory volume in one second (FEV₁) percentpredicted. Approximately 50% of the deaths were cardiovascular in origin. Cumulative survival rates in the CPAP > 6-h group and in the CPAP 1–6 h group were significantly higher than the CPAP < 1 h group, p< 0.00005 and p= 0.01, respectively. Adapted from Campos-Rodriguez et al. [41].

Figure 4

The pathophysiological mechanisms by which OSA can lead to adverse cardiovascular outcomes. SNA: sympathetic neural activity, HR: heart rate, BP: blood pressure, HTN: hypertension, LV: left ventricle, O₂: oxygen, PaO₂: partial pressure of oxygen, PaCO₂: partial pressure of carbon dioxide. Reprinted with permission from Leung RS et al. [111].

Figure 5

Putative mechanisms linking OSA and sleep fragmentation/loss with insulin resistance and increased risk of Type 2 diabetes. Reprinted with permission from Ip M et al. [127].

Figure 6

Algorithmic approach to using portable monitoring to diagnose OSA as proposed by the American Academy of Sleep Medicine [13].