Financial Incentive Increases CPAP Acceptance in Patients from Low Socioeconomic Background

OBJECTIVE: We explored whether financial incentives have a role in patients' decisions to accept (purchase) a continuous positive airway pressure (CPAP) device in a healthcare system that requires cost sharing. DESIGN: Longitudinal interventional study. PATIENTS: The group receiving financial incentive (n = 137, 50.8±10.6 years, apnea/hypopnea index (AHI) 38.7±19.9 events/hr) and the control group (n = 121, 50.9±10.3 years, AHI 39.9±22) underwent attendant titration and a two-week adaptation to CPAP. Patients in the control group had a co-payment of $330-660; the financial incentive group paid a subsidized price of$55. RESULTS: CPAP acceptance was 43% greater (p = 0.02) in the financial incentive group. CPAP acceptance among the low socioeconomic strata (n = 113) (adjusting for age, gender, BMI, tobacco smoking) was enhanced by financial incentive (OR, 95% CI) (3.43, 1.09-10.85), age (1.1, 1.03-1.17), AHI (>30 vs. <30) (4.87, 1.56-15.2), and by family/friends who had positive experience with CPAP (4.29, 1.05-17.51). Among average/high-income patients (n = 145) CPAP acceptance was affected by AHI (>30 vs. <30) (3.16, 1.14-8.75), living with a partner (8.82, 1.03-75.8) but not by the financial incentive. At one-year follow-up CPAP adherence was similar in the financial incentive and control groups, 35% and 39%, respectively (p = 0.82). Adherence rate was sensitive to education (+yr) (1.28, 1.06-1.55) and AHI (>30 vs. <30) (5.25, 1.34-18.5). CONCLUSIONS: Minimizing cost sharing reduces a barrier for CPAP acceptance among low socioeconomic status patients. Thus, financial incentive should be applied as a policy to encourage CPAP treatment, especially among low socioeconomic strata patients

A Network-Based Approach to Prioritize Results from Genome-Wide Association Studies

Genome-wide association studies (GWAS) are a valuable approach to understanding the genetic basis of complex traits. One of the challenges of GWAS is the translation of genetic association results into biological hypotheses suitable for further investigation in the laboratory. To address this challenge, we introduce Network Interface Miner for Multigenic Interactions (NIMMI), a network-based method that combines GWAS data with human protein-protein interaction data (PPI). NIMMI builds biological networks weighted by connectivity, which is estimated by use of a modification of the Google PageRank algorithm. These weights are then combined with genetic association p-values derived from GWAS, producing what we call ‘trait prioritized sub-networks.’ As a proof of principle, NIMMI was tested on three GWAS datasets previously analyzed for height, a classical polygenic trait. Despite differences in sample size and ancestry, NIMMI captured 95% of the known height associated genes within the top 20% of ranked sub-networks, far better than what could be achieved by a single-locus approach. The top 2% of NIMMI height-prioritized sub-networks were significantly enriched for genes involved in transcription, signal transduction, transport, and gene expression, as well as nucleic acid, phosphate, protein, and zinc metabolism. All of these sub-networks were ranked near the top across all three height GWAS datasets we tested. We also tested NIMMI on a categorical phenotype, Crohn’s disease. NIMMI prioritized sub-networks involved in B- and T-cell receptor, chemokine, interleukin, and other pathways consistent with the known autoimmune nature of Crohn’s disease. NIMMI is a simple, user-friendly, open-source software tool that efficiently combines genetic association data with biological networks, translating GWAS findings into biological hypotheses