Why sleep apnea deserves priority in public health: a call to action
Notice bibliographique
Résumé
While perspectives have emphasized the importance of addressing sleep health across the life course and promoting healthy sleep habits in children and youth, less attention has been paid to the public health burden posed by diagnosable sleep disorders, such as obstructive sleep apnea (OSA). Yet, just as social and environmental determinants shape sleep health early in life, they also intersect with biological vulnerabilities and structural barriers that influence the risk, recognition, and treatment of OSA into adulthood and older age. Adequate attention to sleep disorders within public health could help reduce these disparities as they accumulate across the life course. To advance sleep health equity in a meaningful way, public health strategies must include timely identification and management of sleep disorders that disproportionately affect marginalized populations and drive downstream health inequities. OSA is an underdiagnosed and prevalent condition, especially in middle-aged and older adults, with a disproportionate burden in marginalized populations [1]. It affects more than 900 million adults globally, with an estimated prevalence of 1%–5% in young to school-aged children, 9%–38% in adults, and 35%–60% in older individuals, which varies depending on OSA diagnostic criteria, social and environmental factors, and population characteristics (such as age, gender, body mass index, comorbidities, and race and ethnicity). Despite its high prevalence, estimates suggest that up to 80%–90% of individuals with OSA remain undiagnosed, with disproportionately higher rates among women, racial and ethnic minorities, and low-income, rural or underserved communities [2]. OSA is a heterogeneous condition influenced by multiple factors that evolve across the lifespan, reflecting a shift in underlying mechanisms, risk factors and overall health status with age. For example, while anatomical factors (e.g. tonsillar hypertrophy) tend to predominate in early childhood, excess body weight becomes a key contributor in adolescence and middle-aged adults. Notably, excess body weight is socially patterned, often reflecting limited access to resources that support healthy decisions and behaviours, including residing in food deserts and other environments where healthy food and opportunities for physical activity are scarce. Among older adults, the risk of OSA rises alongside comorbidities, such as cardiometabolic and neurological, which compromise airway stability and ventilatory control. Social and environmental factors also transform over time, shaping exposures that impact sleep health [2]. Intermittent hypoxemia and sleep disruption from OSA trigger sympathetic nervous system activation, systemic inflammation, and metabolic disturbances, contributing to the development and progression of chronic diseases such as hypertension, cardiovascular disease, diabetes, mood disorders, and cognitive impairment. Without timely and appropriate treatment, individuals with OSA face increased risks of poor quality of life, impaired school and job performance, comorbidity, mortality, motor vehicle crashes, occupational accidents and higher healthcare use. Furthermore, social and environmental disparities, including socioeconomic status, systemic racism, neighbourhood segregation, geography, access to care and cultural beliefs, contribute to sleep health disparities, OSA recognition, health literacy, and treatment access and adherence [1, 3, 4]. OSA is more prevalent, severe and more often undiagnosed among individuals facing neighbourhood disadvantage, low income, and racial or ethnic marginalization, with stronger associations observed in younger individuals. Within a socioecological framework, multilevel social determinants of health—spanning institutional, neighbourhood, household, interpersonal, and individual levels—interact dynamically across the lifespan to shape disparities in OSA natural history and long-term consequences [2]. These multilevel influences likely contribute through intermediate pathophysiological pathways, including nasopharyngeal and systemic inflammation, impaired lung function, and altered ventilatory control, further exacerbated by chronic physiological and social stress [2]. As a highly prevalent yet often untreated condition in marginalized populations, OSA may reinforce and widen existing health disparities across the life course. Despite growing evidence of its health and societal consequences, OSA is still often viewed as a clinical problem rather than a public health priority. Limited screening access, underdiagnosis, and treatment delays disproportionately affect already vulnerable populations. A shift toward a public health approach is needed to promote early detection, equitable diagnostics, and targeted strategies to address the root causes of disparities in OSA burden and care. Potential public health-relevant solutions include: expanding the role of alternative care providers in OSA care, such as multidisciplinary models and primary care health professionals, by equipping them with the necessary sleep medicine knowledge, skill, and responsibility to introduce universal OSA screening in primary care settings, thereby enhancing equitable identification of OSA symptoms across diverse racial, ethnic, and socioeconomic groups; proactive screening in high-risk populations by tailoring risk-factor assessments across race and ethnicity groups by implementing culturally tailored OSA screening tools and education; improving accessibility to OSA diagnosis and treatment by advancing remote technology, such as at-home OSA detection solutions and telemedicine [2]; and support coordinated efforts—such as task forces, policy initiatives, and community partnerships—to improve sleep health awareness and literacy about OSA in schools and workplaces while working with local businesses, housing agencies, and public health officials to promote healthy sleep practices and conditions across communities; promoting health equity interventions targeting OSA- and sleep-centered lifestyle approaches, including weight loss, diet, exercise and cessation of smoking. Over time, OSA management has evolved from focusing on diagnosis to a more comprehensive chronic care model emphasizing personalized, patient-centered approaches [5]. This shift reflects a growing recognition of the heterogeneous nature of OSA and the importance of aligning care with individual patient characteristics, preferences, and life circumstances. Key components of this evolving model include: (i) tailoring treatment to individual needs, such as considering alternative OSA-targeted therapies and accessible, self-administered interventions; (ii) incorporating individual values and preferences through shared decision-making; (iii) enhancing education and support to improve adherence—via self-guided tools, peer-to-peer support, and culturally tailored resources [1]; (iv) promoting patient engagement through the use of mobile apps, wearable technologies, patient forums, and advocacy initiatives; (v) optimizing care coordination across healthcare providers and settings; and (vi) assessing patient-centered outcomes to guide iterative improvements in care. Together, these strategies aim to improve long-term management, quality of life, and health outcomes for individuals with OSA. To address the high prevalence and under-recognition of OSA as a public health priority, healthcare models must be reimagined by integrating emerging technologies and transforming traditional healthcare roles. A life course perspective, from early life through older adulthood, is essential to understanding the evolving risk factors, adverse outcomes, and disparities associated with OSA. Advancing equity in sleep health will require the use of Artificial Intelligence (AI)-enabled diagnostics, remote monitoring, and culturally tailored, community-based interventions to close gaps in access and ensure personalized, timely care for racialized and underserved populations while also recognizing and addressing the risk that algorithmic bias could further exacerbate existing disparities. Multilevel strategies that confront structural racism and social disadvantage across settings, such as homes, schools, workplaces, and neighborhoods, are critical for equitable OSA screening, diagnosis, and treatment. Moving forward, both mechanistic and interventional research must prioritize underrepresented populations and explore modifiable pathways to guide effective, targeted solutions. Applying implementation science frameworks will also be essential to accelerate the adoption of proven, cost-effective interventions into routine clinical and public health practice. Canadian Sleep Research Consortium and Sleep Health Equity Research team. No conflict of interest to disclose. Not applicable.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,021 | 0,051 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,003 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,008 | 0,015 |
| Communication savante | 0,015 | 0,031 |
| Science ouverte | 0,006 | 0,012 |
| Intégrité de la recherche | 0,051 | 0,053 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,033 | 0,009 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».