The underlying evidence comes from the Medical Expenditure Panel Survey, administered by the US Agency for Healthcare Research and Quality, covering the years 2017 through 2022. This survey is unusual in that it captures data from households themselves as well as from their medical providers, including clinics, hospitals, home health agencies, and pharmacies, along with a sample of insurance plans offered by employers. Crucially, the analysis adjusted for covariates and comorbidities that either facilitate or impede access to health care, or that influence the perceived need for care. That methodological rigor matters, because the question of whether sleep disorders drive excess spending, or whether sicker children simply use more services of every kind, is exactly the kind of confounding that national datasets can help untangle when handled carefully.

The new US figures echo what researchers in Israel documented more than two decades ago. Studies published in the early 2000s showed that children with obstructive sleep apnea syndrome used health care services at markedly higher rates than their peers, and, strikingly, that health care utilization and expenditures fell after the children were treated, often with adenotonsillectomy. Davidson Ward suggests this pattern may hold true across the full spectrum of pediatric sleep disorders: diagnose and treat the sleep problem, and downstream medical costs decline. If that generalizes, the economic case for early identification becomes compelling, because the alternative is paying indefinitely for the complications of an untreated, and frequently reversible, condition.

Yet the 2.88 billion dollar figure almost certainly understates the true cost. The commentary points out that the study did not capture indirect costs such as missed school time, impaired quality of life for the child, and lost wages when parents stay home to care for a chronically sleepless child. Spending on over-the-counter sleep aids and related medications was also excluded. In addition, the survey window included the early years of the COVID-19 pandemic, when families may have avoided seeking care altogether, potentially suppressing recorded expenditures. Even with these limitations, the study’s central message stands: identifying and managing sleep disorders in children is one of those rare interventions that promises both fiscal savings and broad gains in well-being.

So what should be done? The commentary lays out a multi-pronged agenda, beginning with research. Davidson Ward calls for exploring the causes and management of pediatric sleep disorders through both new and existing data, and notes hopefully that the HHS Make America Healthy Again report on children’s well-being mentions sleep health several times, mainly in the context of screen time competing with sleep time. A particular gap involves pediatric insomnia, especially in children with special needs such as autism, where even evidence-based behavioral interventions can fall short. Researchers are encouraged to follow the activities of the National Center on Sleep Disorders Research, which maintains a listserv announcing funding opportunities in the field.

Access to care is the second pillar, and it starts with education. An international survey cited in the commentary found that sleep education in pediatric residency programs is minimal, and that this gap leaves pediatricians reporting unease with treating sleep disorders in children. The proposed fixes are structural: the American Board of Pediatrics and the Accreditation Council for Graduate Medical Education are partnering to shorten pediatric fellowships for trainees aiming to become clinician educators, which could enlarge the applicant pool and make the extra year of sleep medicine fellowship more attractive. Meanwhile, the Advancing Innovation in Residency Education initiative allows pulmonary fellows to train in sleep medicine concurrently with their primary fellowship, and permits board certified or board eligible physicians in relevant specialties to train part-time as fellows. The goal is to expand a pediatric sleep medicine workforce that currently numbers only around 270 specialists nationwide, and the commentary suggests the model could extend to neurology, psychiatry, and otolaryngology.

Technology offers a third route to wider access. Telehealth, the commentary notes, removes the burden of travel for families and allows clinical services to expand without the overhead of brick-and-mortar clinics; the American Academy of Sleep Medicine describes telehealth as having immediate potential to dramatically increase sleep medicine accessibility and clinical efficacy. Diagnostics are another bottleneck. In-laboratory polysomnography remains the gold standard for diagnosing sleep-disordered breathing, but wait times are long. Evidence that home sleep apnea testing is applicable to children and adolescents is accumulating, and forthcoming clinical practice guidelines from the AASM could support insurance coverage for the approach, freeing pediatric sleep laboratories to concentrate on the most complicated cases. The commentary also advocates for wearable devices designed and tested specifically for children, with FDA approval and insurance coverage from the outset, rather than laborious post-market comparisons against adult-oriented gold standards.

Efficiency gains could come from artificial intelligence as well. AI tools that assist in the analysis of polysomnographic data, the commentary argues, should free up clinician time for patient care, easing a workflow in which manual sleep study scoring is notoriously labor intensive. On the therapy side, the commentary urges expansion of proven treatments: hypoglossal nerve stimulation is FDA approved for adolescents with Down syndrome, and Davidson Ward asks why it should not be available for other children who cannot tolerate mask-based positive pressure therapy. Palatal expansion shows promise for relieving obstructive sleep apnea and, she suggests, might be a covered benefit for children on public insurance. She also calls for children to be included in clinical trials of pharmacologic therapies, noting that off-label treatment creates insurance barriers and breeds understandable skepticism among parents, patients, and providers, and recommends that sleep medications be incorporated into the Pediatric Trials Network.

Finally, the commentary makes an explicit appeal for advocacy, aimed at clinicians and the public alike. It points readers to the National Sleep Foundation’s Best Slept Sleep for a Stronger Nation campaign, the AASM’s Sleep for a Stronger Nation initiative and its Community Sleep Health and Public Awareness Grant, and the annual Health Datapalooza sponsored by AcademyHealth, which convenes leaders from government, industry, academia, and the patient community to explore emerging data applications. Clinicians are encouraged to give Grand Rounds, serve as resources for local school boards and child advocacy groups, and support organizations that lobby for sleep health. The broader vision is societal: age-appropriate school start times, help for families managing the conflict between screen time and bedtime, and reduced risk of drowsy driving among teenagers, all of which would create an environment that supports sleep rather than undermining it.

The stakes extend well beyond the health care ledger. As the commentary emphasizes, healthy sleep benefits every domain of a child’s development, from learning and mood to long-term cardiometabolic health, and untreated sleep disorders raise the risk of comorbidities that compound costs over a lifetime. The convergence of new national expenditure data, federal open data initiatives under the OPEN Government Data Act, workforce reforms in training, and maturing home diagnostic technologies gives the field an unusual window of opportunity. The closing sentiment of the commentary is simple and, for a field that has long operated in the shadow of adult sleep medicine, quietly defiant: those who practice pediatric sleep medicine should be proud of their role, because they help children sleep, and in doing so they help families, schools, and the health system as a whole.

Subject of Research: Health care costs and treatment access for pediatric sleep disorders in the United States

Article Title: Pediatric sleep disorders are common and costly: how can we help?

Article References: Ward, S. L. D. (2026). Pediatric sleep disorders are common and costly: how can we help?. Journal of Clinical Sleep Medicine, 22(1), Article 127. https://doi.org/10.1007/s44470-026-00152-1

Image Credits: AI Generated

DOI: 10.1007/s44470-026-00152-1

Keywords: pediatric sleep disorders, obstructive sleep apnea, health care expenditures, polysomnography, home sleep apnea testing, telehealth, sleep medicine workforce, hypoglossal nerve stimulation, pediatric insomnia, Medical Expenditure Panel Survey, sleep health advocacy, artificial intelligence

The distinction is not academic hair-splitting; it reflects two fundamentally different failures of the respiratory system. In obstructive sleep apnea, the sleeper’s airway physically collapses even as the brain keeps driving the breathing muscles—the effort continues, but airflow falters against a blocked conduit. In central sleep apnea, the problem lies upstream: neural output from the brainstem’s respiratory centers wanes, and airflow falls because the drive to breathe itself has been withdrawn. Central events arise from instability in the ventilatory control system, often quantified by an engineering concept known as loop gain. A system with high loop gain overreacts to fluctuations in carbon dioxide, overshooting corrections and oscillating into periodic breathing, whereas low loop gain indicates a stable control system resistant to such instability. Both obstructive and central apnea patients can exhibit elevated loop gain, and the degree of abnormality may predict how a given patient responds to different treatments.

While scoring rules for frank apneas—complete pauses in breathing—are well agreed upon, the AASM scoring manual treats classification of hypopneas as optional, and most sleep centers simply do not perform it. The panel argues this default has real consequences. In a large French cohort of more than 2,000 people with sleep apnea, the prevalence of central sleep apnea jumped from 5 percent to nearly 20 percent once hypopneas, in addition to apneas, were manually classified. That fourfold increase suggests a substantial population of patients whose underlying central ventilatory instability has been invisible to their diagnoses because their shallow breaths were assumed to be obstructive.

How, then, can a scorer tell the two event types apart on a routine polysomnogram? The current AASM criteria list snoring, flattening of the nasal pressure waveform, and thoracoabdominal desynchrony—out-of-phase motion of the chest and abdominal belts—as hallmarks of obstruction; when none of these features is present, the event should be scored as central. Winfried Randerath and colleagues developed a more elaborate algorithm, validated against esophageal manometry, the gold standard for measuring respiratory effort. Their stepwise approach adds the shape of the recovery breaths that follow an event, the position of the arousal relative to the recovery breaths, and the sleep stage in which the event occurs. Against esophageal pressure measurements, the algorithm correctly identified 60.5 percent of obstructive hypopneas and 76.9 percent of central hypopneas, for an overall accuracy of 68 percent. The two most informative signals in both frameworks are inspiratory flow flattening and respiratory paradox, with published sensitivities and specificities of 0.65 and 0.67 for flow flattening and 0.81 and 0.46 for paradox, underscoring that no single channel suffices and careful integration of multiple signals is essential.

Further physiological markers have been proposed by Javaheri and colleagues. Synchronous changes in airflow and effort, snoring during the recovery breaths rather than during the event itself, and a symmetric desaturation-resaturation pattern on the oximetry trace point toward central disease, whereas snoring during the event, arousals at event termination, and progressive prolongation of the inspiratory duty cycle—the fraction of the breath devoted to inspiration—characterize obstruction. That last feature has been confirmed experimentally in studies using inspiratory resistive loading, which mimics airway obstruction and reliably lengthens inspiratory time. A head-to-head comparison by Dupuy-McCauley and colleagues found the AASM and Randerath methods performed similarly, at 67 to 69 percent accuracy, but with only fair interrater reliability, a kappa of 0.30—meaning two trained scorers often disagree. Signal quality, particularly the notoriously variable nasal pressure signal, is a likely culprit.

To move the field forward, the panel proposes an operational definition: a central hypopnea shows no snoring during the event, waxing-and-waning airflow during recovery breaths, waxing-and-waning effort in the belts, and an arousal occurring in the middle of the recovery breaths rather than at their end. Just as importantly, it proposes a new category, the unclassified or indeterminate hypopnea, for events that meet no strict criteria, rather than forcing them into the obstructive bucket. Under this framework, a sleep report would include the overall apnea-hypopnea index along with separate obstructive, central, and unclassified indices that sum to the total. The panel stresses this is a provisional construct, not a guideline, and requires prospective validation.

Why does this matter now? Because sleep medicine is entering an era of mechanistically targeted therapies, and each therapy aims at a different piece of the physiology. Transvenous phrenic nerve stimulation, which paces the diaphragm to stabilize breathing, works on central ventilatory instability; hypoglossal nerve stimulation, which protrudes the tongue to hold the airway open, works on obstruction. In the pivotal trial of phrenic nerve stimulation, the likelihood of achieving at least a 50 percent reduction in the apnea-hypopnea index rose steadily with the baseline proportion of central events—from 37.5 percent when fewer than half of events were central to 76.5 percent when 90 percent or more were central. When hypopneas were classified retrospectively in this cohort, most residual events after implantation were found to be obstructive, and intriguingly, the proportion of central apneas did not correlate with the proportion of central hypopneas, meaning the two event types must be characterized independently rather than assumed to travel together. The same logic applies in reverse for hypoglossal nerve stimulation: a patient with a heavy load of unrecognized central hypopneas is a candidate for a disappointing result.

The stakes extend to routine positive airway pressure care. The AASM’s recent clinical practice guideline recommends adaptive servo-ventilation for central sleep apnea in patients with normal heart function when CPAP fails; if central events have been correctly enumerated from the outset, prolonged, fruitless trials of CPAP could potentially be avoided. The panel also draws attention to treatment-emergent central sleep apnea, which arises in roughly 4 to 19 percent of obstructive sleep apnea patients during CPAP initiation and persists in about one-third of those affected after months of therapy. Distinguishing pre-existing, treatment-resistant central disease from events that genuinely emerge after CPAP initiation matters for prognosis and management: emergent events are often transient and warrant re-evaluation after a few weeks, whereas persistent disease may respond to adaptive servo-ventilation, supplemental oxygen, or the carbonic anhydrase inhibitor acetazolamide. Longitudinal vigilance is essential, since central apnea can develop years into CPAP therapy with the onset of atrial fibrillation or left ventricular dysfunction, or with the use of opioids, baclofen, or ticagrelor—exposures that should prompt a fresh look at the pattern of breathing disturbances.

The panel is candid about the obstacles. Manual classification adds time and training demands for sleep technologists, and visual assessment suffers from inconsistent interrater reliability across centers. Overclassifying central hypopneas risks inappropriate therapy escalation and added cost; underclassifying them risks residual symptoms and missed combination therapy. The solution, many panelists believe, lies in automation. Several developmental pathways were outlined: event-level analysis extracting features from airflow, effort belts, diaphragmatic EMG, snoring, EEG, and heart rate signals; subject-level metrics such as central and obstructive indices that exploit the characteristic low cycle-length variability and sinusoidal airflow of central apnea, using tools like wavelet decomposition, cardiopulmonary coupling, and machine learning; multimodal models incorporating medications, comorbidities, and demographics; counterfactual prediction to resolve ambiguous events; and approaches that directly model therapeutic response. All of these depend on a prerequisite the panel emphasizes: harmonized, minimum signal-quality standards across laboratories, because algorithms trained on noisy, inconsistent signals will not generalize. With validation studies and human oversight, the panel concludes, routine hypopnea classification could become the foundation of a genuinely precision-based approach to sleep-disordered breathing, matching each patient’s endophenotype to the therapy most likely to quiet their nights and restore their days.

Subject of Research: Classification of obstructive versus central hypopneas in sleep-disordered breathing diagnosis and therapy selection

Article Title: The importance of classifying central hypopneas: a call to action

Article References: Ahn, A. P., Azarbarzin, A., Badr, M. S., Berry, R., DeYoung, P., Dupuy-McCauley, K., Morgenthaler, T. I., Pépin, J. L., Randerath, W., Sands, S., Tallavajhula, S., & Malhotra, A. (2026). The importance of classifying central hypopneas: a call to action. Journal of Clinical Sleep Medicine, 22(1), Article 149. https://doi.org/10.1007/s44470-026-00177-6

Image Credits: AI Generated

DOI: 10.1007/s44470-026-00177-6

Keywords: central sleep apnea, obstructive sleep apnea, hypopnea classification, polysomnography, loop gain, phrenic nerve stimulation, hypoglossal nerve stimulation, adaptive servo-ventilation, CPAP, treatment-emergent central sleep apnea, machine learning, sleep medicine