Socioeconomic Inequities in Adherence to Positive Airway Pressure Therapy in Population-Level Analysis.
Abhishek PandeySuresh MereddyDaniel CombsSafal ShettySalma I PatelSaif MashaqAzizi A SeixasKerry LittlewoodGirardin Jean-LuisSairam ParthasarathyPublished in: Journal of clinical medicine (2020)
(a) Background: In patients with sleep apnea, poor adherence to positive airway pressure (PAP) therapy has been associated with mortality. Regional studies have suggested that lower socioeconomic status is associated with worse PAP adherence but population-level data is lacking. (b) Methods: De-identified data from a nationally representative database of PAP devices was geo-linked to sociodemographic information. (c) Results: In 170,641 patients, those in the lowest quartile of median household income had lower PAP adherence (4.1 + 2.6 hrs/night; 39.6% adherent by Medicare criteria) than those in neighborhoods with highest quartile median household income (4.5 + 2.5 hrs/night; 47% adherent by Medicare criteria; p < 0.0001). In multivariate regression, individuals in neighborhoods with the highest income quartile were more adherent to PAP therapy than those in the lowest income quartile after adjusting for various confounders (adjusted Odds Ratio (adjOR) 1.18; 95% confidence interval (CI) 1.14, 1.21; p < 0.0001). Over the past decade, PAP adherence improved over time (adjOR 1.96; 95%CI 1.94, 2.01), but health inequities in PAP adherence remained even after the Affordable Care Act was passed. (d) Conclusion: In a nationally representative population, disparities in PAP adherence persist despite Medicaid expansion. Interventions aimed at promoting health equity in sleep apnea need to be undertaken.
Keyphrases
- sleep apnea
- positive airway pressure
- obstructive sleep apnea
- affordable care act
- mental health
- physical activity
- healthcare
- public health
- health insurance
- glycemic control
- end stage renal disease
- newly diagnosed
- electronic health record
- ejection fraction
- emergency department
- prognostic factors
- adipose tissue
- machine learning
- depressive symptoms
- coronary artery disease
- insulin resistance
- risk assessment
- data analysis
- risk factors
- cardiovascular events
- human health
- mesenchymal stem cells
- deep learning
- cell therapy