BACKGROUND: Excessive @entity25 affects nearly 20% of the general population and is associated with many medical conditions, including @entity1442 ( @entity1442 ), @entity27 ( @entity27 ), and @entity2724 . @entity25 imposes a significant clinical, quality-of-life, safety, and economic burden on society. OBJECTIVE: To compare healthcare costs for @entity1 receiving initial therapy with @entity2728 or with @entity2728 for the treatment of @entity25 associated with @entity27 , @entity1442 , or @entity2724 . METHODS: A retrospective cohort analysis of medical and pharmacy claims was conducted using the IMS LifeLink Health Plan Claims Database. @entity1 aged >= 18 years who had a pharmacy claim for @entity2728 or for @entity2728 between June 1, 2009, and February 28, 2012, and had 6 months of continuous eligibility before the index prescription date, as well as International Classification of Diseases, Ninth Revision diagnosis for either @entity27 (327.23), @entity1442 (327.36), or @entity2724 (347.0x) were included in the study. @entity1 were placed into 1 of 2 treatment cohorts based on their index prescription and followed for 1 month minimum and 34 months maximum. The annualized all-cause costs were calculated by multiplying the average per-month medical and pharmacy costs for each @entity1 by 12 months. The daily average consumption (DACON) for @entity2728 or for @entity2728 was calculated by dividing the total units dispensed of either drug by the prescription days supply. RESULTS: A total of 5693 @entity1 receiving @entity2728 and 9212 @entity1 receiving @entity2728 were included in this study. A lower DACON was observed for @entity2728 (1.04) compared with @entity2728 (1.47). The postindex mean medical costs were significantly lower for the @entity2728 cohort compared with the @entity2728 cohort after adjusting for baseline differences ( 11,363 vs  13,775, respectively; P = .005). The mean monthly drug-specific pharmacy costs were lower for the @entity2728 cohort compared with the @entity2728 cohort ( 166 vs  326, respectively; P <.001). In addition, lower total healthcare costs were observed for the @entity2728 cohort compared with the @entity2728 cohort after correcting for baseline differences ( 18,309 vs  23,530, respectively; P <.001). CONCLUSION: As shown in this analysis, @entity2728 may have real-world DACON advantages and may be associated with lower overall healthcare costs compared with @entity2728 .

Healthcare costs among @entity1 with XXXX associated @entity27 , @entity1442 , or @entity2724 .

@entity1 :: ('9606', 'Species') :: ['Patients', 'patients', 'patient'] @entity27 :: ('MESH:D020181', 'Disease') :: ['obstructive sleep apnea', 'OSA'] @entity2724 :: ('MESH:D009290', 'Disease') :: ['narcolepsy'] @entity1442 :: ('MESH:D020178', 'Disease') :: ['shift work disorder', 'SWD'] @entity2728 :: ('MESH:C048833', 'Chemical') :: ['armodafinil', 'modafinil'] @entity25 :: ('MESH:D012893', 'Disease') :: ['excessive sleepiness', 'daytime sleepiness', 'Excessive sleepiness']
@entity25 :: (MESH:D012893,Disease) :: ['excessive sleepiness']
