Objective: To assess asthma control and associations with health-related quality of life (HRQoL) and economic outcomes among patients with asthma and allergic comorbidities treated with inhaled corticosteroids (ICS) and long-acting beta-agonists (LABA) combination therapy. Methods: Data from the 2011-2013 US National Health and Wellness Survey were used to identify patients with asthma currently treated with ICS and LABA combination therapy (N = 1923). Patients were included if they selfreported a physician diagnosis of asthma and at least one allergic/asthma-related comorbid condition (e.g., nasal allergies, atopic dermatitis). Asthma Control Test scores categorized patients as very poorly (scores ࣘ 15; 29.3%), not well (16-19; 25.1%), or well controlled (20-25; 45.7%). Outcomes included HRQoL (SF-36v2; SF-12v2), work productivity and activity impairment, healthcare utilization (HRU), and annual indirect and direct costs. Generalized linear models, controlling for covariates, examined whether outcomes differed by asthma control. Results: Over half of the patients had very poorly or not well-controlled asthma (54.4%). Patients with very poorly controlled versus well-controlled asthma reported significantly greater decreases in HRQoL, greater overall work impairment, and higher HRU (all, p < 0.05). Very poorly controlled patients incurred over double the indirect costs and nearly one and a half times the direct and total costs of well-controlled patients. Conclusions: Increasing level of asthma control was related to improved HRQoL and lower costs. The considerably high prevalence of uncontrolled asthma among patients on ICS and LABA suggests poor treatment adherence or unmet needs in current treatment and may require step-up therapy in appropriate patients according to clinical guidelines.

BackgroundCluster analysis (CA) is a frequently used applied statistical technique that helps to reveal hidden structures and “clusters” found in large data sets. However, this method has not been widely used in large healthcare claims databases where the distribution of expenditure data is commonly severely skewed. The purpose of this study was to identify cost change patterns of patients with end-stage renal disease (ESRD) who initiated hemodialysis (HD) by applying different clustering methods.MethodsA retrospective, cross-sectional, observational study was conducted using the Truven Health MarketScan® Research Databases. Patients aged ≥18 years with ≥2 ESRD diagnoses who initiated HD between 2008 and 2010 were included. The K-means CA method and hierarchical CA with various linkage methods were applied to all-cause costs within baseline (12-months pre-HD) and follow-up periods (12-months post-HD) to identify clusters. Demographic, clinical, and cost information was extracted from both periods, and then examined by cluster.ResultsA total of 18,380 patients were identified. Meaningful all-cause cost clusters were generated using K-means CA and hierarchical CA with either flexible beta or Ward’s methods. Based on cluster sample sizes and change of cost patterns, the K-means CA method and 4 clusters were selected: Cluster 1: Average to High (n = 113); Cluster 2: Very High to High (n = 89); Cluster 3: Average to Average (n = 16,624); or Cluster 4: Increasing Costs, High at Both Points (n = 1554). Median cost changes in the 12-month pre-HD and post-HD periods increased from $185,070 to $884,605 for Cluster 1 (Average to High), decreased from $910,930 to $157,997 for Cluster 2 (Very High to High), were relatively stable and remained low from $15,168 to $13,026 for Cluster 3 (Average to Average), and increased from $57,909 to $193,140 for Cluster 4 (Increasing Costs, High at Both Points). Relatively stable costs after starting HD were associated with more stable scores on comorbidity index scores from the pre-and post-HD periods, while increasing costs were associated with more sharply increasing comorbidity scores.ConclusionsThe K-means CA method appeared to be the most appropriate in healthcare claims data with highly skewed cost information when taking into account both change of cost patterns and sample size in the smallest cluster.

Patients with HF who died within 1 year after an index HF encounter incurred markedly higher costs within 1 year (despite the much shorter post-index period) and PPPM costs than those who survived, with the majority of costs attributable to hospitalizations for both patient cohorts. There may be opportunities for improving outcomes in HF, considering higher use of pharmacotherapy and lower costs were seen among survivors.

Patients with HF experienced ≥ 2.8-fold increase in monthly all-cause total cost over the last 6 months of life, which was driven by hospitalization. Although MAPD enrollees incurred greater cost increases, cumulative costs were higher for commercial enrollees. After multivariable adjustment, older age, comorbid coronary heart disease, and no evidence of HFRx were among factors associated with higher risk of cost increase over the last 6 months of life. Study findings provide predeath cost information that should be useful in value assessments of innovative HF interventions and highlight impact of HFRx on predeath health care costs.