Subtopic Deep Dive
Epidemiological Prevalence Studies
Research Guide
What is Epidemiological Prevalence Studies?
Epidemiological Prevalence Studies in Obstructive Sleep Apnea Research are population-based investigations that quantify OSA occurrence using validated questionnaires and objective measures like polysomnography.
These studies track OSA rates across demographics, linking trends to risk factors such as obesity. Key works include Peppard et al. (2013) reporting increased prevalence tied to obesity (4474 citations) and Young et al. (2002) documenting high undiagnosed cases (4178 citations). Global estimates appear in Benjafield et al. (2019) with 3668 citations.
Why It Matters
Prevalence data from Peppard et al. (2013) inform public health screening by showing obesity-driven OSA rise from 17% to 28% in men over a decade. Young et al. (2002) highlight undiagnosed OSA's morbidity burden, guiding policy for at-risk groups. Benjafield et al. (2019) estimate 936 million cases worldwide, driving resource allocation for diagnosis and treatment.
Key Research Challenges
Underestimation from Questionnaires
Validated tools like questionnaires overestimate mild OSA but miss severe cases without PSG confirmation (Young et al., 2002). Actigraphy aids monitoring but lacks PSG accuracy (Ancoli-Israel et al., 2003). Population studies struggle with low response rates.
Global Variability in Estimates
Prevalence differs by region due to inconsistent diagnostic criteria and obesity rates (Benjafield et al., 2019). Senaratna et al. (2016) systematic review finds 22% general population OSA but methodological heterogeneity limits comparisons (2348 citations). Standardization remains unresolved.
Undiagnosed Case Quantification
Only 10-20% of OSA cases receive clinical diagnosis in adults (Young et al., 1997; 1740 citations). Questionnaire follow-ups reveal gaps in employed populations. Linking self-reports to objective data poses logistical barriers.
Essential Papers
Increased Prevalence of Sleep-Disordered Breathing in Adults
P. E. Peppard, Terry Young, Jodi H. Barnet et al. · 2013 · American Journal of Epidemiology · 4.5K citations
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, prev...
Epidemiology of Obstructive Sleep Apnea
Terry Young, Paul E. Peppard, Daniel J. Gottlieb · 2002 · American Journal of Respiratory and Critical Care Medicine · 4.2K citations
Population-based epidemiologic studies have uncovered the high prevalence and wide severity spectrum of undiagnosed obstructive sleep apnea, and have consistently found that even mild obstructive s...
Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis
Adam Benjafield, Najib Ayas, Peter R. Eastwood et al. · 2019 · The Lancet Respiratory Medicine · 3.7K citations
Obstructive Sleep Apnea as a Risk Factor for Stroke and Death
H. Klar Yaggi, John Concato, Walter N. Kernan et al. · 2005 · New England Journal of Medicine · 2.9K citations
The obstructive sleep apnea syndrome significantly increases the risk of stroke or death from any cause, and the increase is independent of other risk factors, including hypertension.
The Role of Actigraphy in the Study of Sleep and Circadian Rhythms
Sonia Ancoli‐Israel, Roger J. Cole, Cathy Alessi et al. · 2003 · SLEEP · 2.7K citations
In summary, although actigraphy is not as accurate as PSG for determining some sleep measurements, studies are in general agreement that actigraphy, with its ability to record continuously for long...
Sympathetic neural mechanisms in obstructive sleep apnea.
Virend K. Somers, Mark Eric Dyken, M. P. Clary et al. · 1995 · Journal of Clinical Investigation · 2.5K citations
Blood pressure, heart rate, sympathetic nerve activity, and polysomnography were recorded during wakefulness and sleep in 10 patients with obstructive sleep apnea. Measurements were also obtained a...
Prevalence of obstructive sleep apnea in the general population: A systematic review
Chamara V. Senaratna, Jennifer L. Perret, Caroline Lodge et al. · 2016 · Sleep Medicine Reviews · 2.3K citations
Reading Guide
Foundational Papers
Start with Young et al. (2002, 4178 citations) for core epidemiology and undiagnosed OSA morbidity; follow with Peppard et al. (2013, 4474 citations) for obesity-prevalence links.
Recent Advances
Benjafield et al. (2019, 3668 citations) for global estimates; Senaratna et al. (2016, 2348 citations) systematic review of population prevalence.
Core Methods
Polysomnography for AHI (Kushida et al., 2005); actigraphy for field studies (Ancoli-Israel et al., 2003); questionnaires for screening (Young et al., 1997).
How PapersFlow Helps You Research Epidemiological Prevalence Studies
Discover & Search
Research Agent uses searchPapers and citationGraph to map foundational works like Peppard et al. (2013, 4474 citations), revealing clusters around Young et al. (2002). exaSearch uncovers global studies beyond OpenAlex, while findSimilarPapers links Benjafield et al. (2019) to regional prevalence data.
Analyze & Verify
Analysis Agent applies readPaperContent to extract prevalence rates from Young et al. (2002), then verifyResponse with CoVe checks claims against Senaratna et al. (2016). runPythonAnalysis computes meta-analytic odds ratios from extracted tables using pandas, with GRADE grading for evidence quality in cohort designs.
Synthesize & Write
Synthesis Agent detects gaps like undiagnosed proportions post-Young et al. (1997), flagging contradictions in actigraphy vs. PSG (Ancoli-Israel et al., 2003). Writing Agent uses latexEditText for prevalence tables, latexSyncCitations for 10+ papers, and latexCompile for reports; exportMermaid visualizes risk factor networks.
Use Cases
"Compare OSA prevalence rates across Peppard 2013 and Benjafield 2019 with confidence intervals"
Research Agent → searchPapers → Analysis Agent → runPythonAnalysis (pandas meta-analysis of rates/ORs) → CSV export of pooled estimates with CIs.
"Draft a review section on global OSA burden with citations from top 5 prevalence papers"
Synthesis Agent → gap detection → Writing Agent → latexEditText + latexSyncCitations (Young 2002, Peppard 2013 etc.) → latexCompile → PDF with formatted tables.
"Find Python code for actigraphy-based OSA screening from related papers"
Research Agent → paperExtractUrls (Ancoli-Israel 2003) → Code Discovery → paperFindGithubRepo → githubRepoInspect → validated scripts for sleep-wake detection.
Automated Workflows
Deep Research workflow conducts systematic review of 50+ OSA prevalence papers, chaining searchPapers → citationGraph → GRADE-scored report on trends since Young et al. (2002). DeepScan applies 7-step verification to Benjafield et al. (2019) estimates, using CoVe checkpoints and runPythonAnalysis for burden calculations. Theorizer generates hypotheses on undiagnosed OSA trajectories from Peppard et al. (2013) data.
Frequently Asked Questions
What defines Epidemiological Prevalence Studies in OSA?
Population-based studies using questionnaires and PSG to estimate OSA rates across groups, as in Peppard et al. (2013) showing 28% moderate-severe prevalence in obese adults.
What methods do these studies employ?
Validated tools like Berlin Questionnaire plus objective PSG or actigraphy; Young et al. (2002) used cohort PSG for undiagnosed OSA spectrum.
What are key papers?
Peppard et al. (2013, 4474 citations) on obesity trends; Young et al. (2002, 4178 citations) on epidemiology; Benjafield et al. (2019, 3668 citations) on global burden.
What open problems exist?
Standardizing diagnostics across regions (Senaratna et al., 2016) and quantifying undiagnosed cases beyond 80% (Young et al., 1997).
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Part of the Obstructive Sleep Apnea Research Research Guide