Rationale Clear definition of optimal positive airway pressure therapy usage in patients with obstructive sleep apnea is not possible because of scarce data on the relationship between usage hours and major clinical outcomes. Objective To investigate the dose–response relationship between positive airway pressure usage and healthcare resource utilization and determine the minimum device usage required for benefit. Methods A linked data set combined deidentified payer-sourced administrative medical/pharmacy claims data from more than 100 U.S. health plans and individual patient positive airway pressure usage data. Eligible adults (age ⩾18 yr) had a new obstructive sleep apnea diagnosis between June 2014 and April 2018. All received positive airway pressure therapy (AirSense 10; ResMed) with claims data for ⩾1 year before, and 2 years after, device setup. Healthcare resource utilization was determined on the basis of the number of all-cause hospitalizations and emergency room visits over 3, 12, and 24 months after positive airway pressure initiation. Results Data from 179,188 patients showed a clear dose–response relationship between daily positive airway pressure usage and healthcare utilization. Minimum device usage required for benefit was 1–3 hours per night. There was a statistically significant decrease in hospitalizations and emergency room visits at all time points (all P s < 0.0001) with increasing positive airway pressure usage. Each additional hour of usage per night decreased hospitalizations and emergency room visits by 5–10% and 5–7%, respectively. Conclusions These data provide compelling evidence for a dose–response relationship between positive airway pressure usage and healthcare utilization, with benefits seen even when usage was as low as 1–2 hours per night.

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Introduction Health disparities adversely affect people based on characteristics that are historically linked to discrimination or exclusion. There is limited research on understanding consequences of health disparities in the diagnosis and treatment of obstructive sleep apnea (OSA). This retrospective study sought to investigate health disparities affecting OSA patients using real-world data. Methods A national sample of administrative claims data from OSA patients who had a claim for a sleep test (home sleep test or polysomnography) was used. Gender, insurance payer, race/ethnicity, and 3-digit zip code were characterized from the claims data at the time of the first sleep test to describe differences across patients. Results The sample included about 2 million patients with 44.9% female, an average age 51.0 years, and an insurance payer breakdown of 74% commercial, 18% Medicaid, and 8% Medicare Advantage. There were a number of challenges that made it difficult to draw conclusions about the impact of health disparities in this data. The source of gender data was unknown; therefore, we do not know if it represents identified gender or biological sex. Race/ethnicity data was listed as “unknown” for 89% of commercial patients, 18% of Medicaid patients, and 36% of Medicare Advantage patients. Data reporting did not allow for details on multiple races or separation of race and ethnicity. Using 3-digit zip codes proved to be unreliable for drawing conclusions about a patient’s residence and access to care as 3-digit zip code areas are heterogenous and could cover a small section of a major city, a large rural portion of a state, or both. Lastly, ICD-10 Z codes, which describe social determinants of health (SDOH), appear to be underutilized. Conclusion Currently, using administrative claims as a real-world data source to assess health disparities is difficult due to data incompleteness, lack of adequate data definitions, and data aggregations made to protect patient privacy. Strategies to increase usability of claims data for investigating health disparities may include improvements in payer data reporting, increased utilization of SDOH Z codes, and linking to other more patient-centric data sources. Analyzing large samples of real-world data may help identify disparities and improve care for all patients. Support (if any)