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Record W4412779500 · doi:10.1093/eurpub/ckaf092

Why sleep apnea deserves priority in public health: a call to action

2025· article· en· W4412779500 on OpenAlexaffabout
Tetyana Kendzerska, Elizabeth Keys, Dayna A. Johnson

Bibliographic record

VenueEuropean Journal of Public Health · 2025
Typearticle
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusOttawa HospitalOkanagan CollegeUniversity of Ottawa
Fundersnot available
KeywordsAction (physics)Sleep (system call)Sleep apneaMedicineCall to actionApneaPublic healthPsychologyPsychiatryBusinessComputer scienceInternal medicineAdvertisingNursing

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.051
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.051
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0030.002
Science and technology studies0.0080.015
Scholarly communication0.0150.031
Open science0.0060.012
Research integrity0.0510.053
Insufficient payload (model declined to judge)0.0330.009

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.138
GPT teacher head0.395
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes2
Has abstractyes

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