BACKGROUND:The morbidity and mortality associated with COPD exacts a considerable economic burden. Comorbidities in COPD are associated with poor health outcomes and increased costs. Our objective was to assess the impact of comorbidities on COPD-associated costs in a large administrative claims dataset.METHODS:This was a retrospective observational study of data from the Truven Health MarketScan Commercial Claims and Encounters and the MarketScan Medicare Supplemental Databases from January 1, 2009, to September 30, 2012. Resource consumption was measured from the index date (date of first occurrence of non-rule-out COPD diagnosis) to 360 days after the index date. Resource use (all-cause and disease-specific [ie, COPD- or asthma-related] ED visits, hospitalizations, office visits, other outpatient visits, and total length of hospital stay) and health-care costs (all-cause and disease-specific costs for ED visits, hospitalizations, office visits, and other outpatient visits and medical, prescription, and total health-care costs) were assessed. Generalized linear models were used to evaluate the impact of comorbidities on total health-care costs, adjusting for age, sex, geographic location, baseline health-care use, employment status, and index COPD medication.RESULTS:Among 183,681 patients with COPD, the most common comorbidities were cardiovascular disease (34.8%), diabetes (22.8%), asthma (14.7%), and anemia (14.2%). Most patients (52.8%) had one or two comorbidities of interest. The average all-cause total health-care costs from the index date to 360 days after the index date were highest for patients with chronic kidney disease ($41,288) and anemia ($38,870). The impact on total health-care costs was greatest for anemia ($10,762 more, on average, than a patient with COPD without anemia).CONCLUSIONS:Our analysis demonstrated that high resource use and costs were associated with COPD and multiple comorbidities.

BackgroundComparative effectiveness research (CER) often includes observational studies utilizing administrative data. Multiple conditioning methods can be used for CER to adjust for group differences, including difference-in-differences (DiD) estimation.ObjectiveThis study presents DiD and demonstrates how to apply this conditioning method to estimate treatment outcomes in the CER setting by utilizing the MarketScan® Databases for multiple sclerosis (MS) patients receiving different therapies.MethodsThe sample included 6762 patients, with 363 in the Test Cohort [glatiramer acetate (GA) switched to fingolimod (FTY)] and 6399 in the Control Cohort (GA only, no switch) from a US administrative claims database. A trend analysis was conducted to rule out concerns regarding regression to the mean and to compare relapse rates among treatment cohorts. DiD analysis was used to enable comparisons among the Test and Control Cohorts. Logistic regression was used to estimate the probability of relapse after switching from GA to FTY, and to compare group differences in the pre- and post-index periods.ResultsCrude DiD analysis showed that in the pre-index period more patients in the Test Cohort experienced an MS relapse and had a higher mean number of relapses than in the Control Cohort. During the pre-index period, numeric and relative data for MS relapses in patients in the Test Cohort were significantly higher than in the Control Cohort, while no significant between-group differences emerged during the post-index period. Generalized linear modeling with DiD regression estimation showed that the mean number of MS relapses decreased significantly in the post-index period among patients in the Test Cohort compared with patients in the Control Cohort.ConclusionIn this study, an MS population was utilized to demonstrate how DiD can be applied to estimate treatment effects in a heterogeneous population, where the Test and Control Cohorts varied greatly. The results show that DiD offers a robust method for comparing diverse cohorts when other risk-adjustment methods may not be adequate.

This research was funded by Novartis Pharmaceuticals. Johnson, Lin, Ko, and Herrera are employed by Novartis Pharmaceuticals and own Novartis stock. Huanxue Zhou is employed by KMK Consulting, which provides consulting services to Novartis. Study concept and design were contributed by Johnson, Lin, Ko, and Herrera. Zhou collected the data, and data interpretation was performed by Johnson, Lin, Ko, and Herrera. All authors were involved in manuscript revision. The abstract for this study was presented at the AMCP Nexus 2015; October 26-29, 2015; Orlando, Florida.

BackgroundProportion of days covered (PDC), a commonly used adherence metric, does not provide information about the longitudinal course of adherence to treatment over time. Group-based trajectory model (GBTM) is an alternative method that overcomes this limitation.MethodsThe statistical principles of GBTM and PDC were applied to assess adherence during a 12-month follow-up in psoriasis patients starting treatment with a biologic. The optimal GBTM model was determined on the basis of the balance between each model’s Bayesian information criterion and the percentage of patients in the smallest group in each model. Variables potentially predictive of adherence were evaluated.ResultsIn all, 3,249 patients were included in the analysis. Four GBTM adherence groups were suggested by the optimal model, and patients were categorized as demonstrating continuously high adherence, high-then-low adherence, moderate-then-low adherence, or consistently moderate adherence during follow-up. For comparison, four PDC groups were constructed: PDC Group 4 (PDC ≥75%), PDC Group 3 (25%≤ PDC <50%), PDC Group 2 (PDC <25%), and PDC Group 1 (50%≤ PDC <75%). Our findings suggest that the majority of patients (97.9%) from PDC Group 2 demonstrated moderate-then-low adherence, whereas 96.4% of patients from PDC Group 4 showed continuously high adherence. The remaining PDC-based categorizations did not capture patients with uniform adherence behavior based on GBTM. In PDC Group 3, 25.3%, 17.2%, and 57.5% of patients exhibited GBTM-defined consistently moderate adherence, moderate-then-low adherence, or high-then-low adherence, respectively. In PDC Group 1, 70.8%, 23.6%, and 5.7% of patients had consistently moderate adherence, high-then-low adherence, and continuously high adherence, respectively. Additional analyses suggested GBTM-based categorization was best predicted by patient age, sex, certain comorbidities, and particular drug use.ConclusionGBTM is a more appropriate way to model dynamic behaviors and offers researchers an alternative to more traditional drug adherence measurements.

PurposeThe all-cause readmission rate within 30 days of index admissions for chronic obstructive pulmonary disease (COPD) was approximately 21% in the United States in 2008. This study aimed to examine patient and clinical characteristics predicting 30-day unplanned readmissions for an initial COPD hospitalization and to determine those predictors’ importance.Patients and methodsA retrospective study was conducted in patients with COPD-related hospitalizations using commercial claims data from 2010 to 2012. The primary outcome was all-cause unplanned readmission, with secondary outcomes being COPD as primary diagnosis and COPD as any diagnosis unplanned readmissions. Factors predicting unplanned readmissions encompassed demographic, pharmacy, and medical variables identified at baseline and during the index hospitalization. Dominance analysis was conducted to rank the predictors in terms of importance, defined as the contribution to change in model fit of a predictor by itself and in combination with other predictors.ResultsAfter applying the inclusion and exclusion criteria, 18,282 patients with index COPD-related admissions were identified. Among them, the rates of unplanned readmissions with COPD as primary diagnosis, COPD as any diagnosis, and all-cause were 2.6%, 5.6%, and 7.3%, respectively. For each outcome, the readmission group was slightly older, had a greater COPD severity score, and required a longer length of stay. Moreover, the readmission group had larger proportions of patients with comorbidities, dyspnea/shortness of breath, intensive care unit stay, or ventilator use, compared to the non-readmission group. Dominance analysis revealed that the three most important predictors – heart failure/heart disease, anemia, and COPD severity score – accounted for 56% of the predicted variance in all-cause unplanned readmissions.ConclusionOverall, COPD severity score and heart failure/heart disease emerged as important factors in predicting 30-day unplanned readmissions across all three outcomes. Results from dominance analysis suggest looking beyond COPD-specific complications and focusing on comorbid conditions highly associated with COPD in order to lower all-cause unplanned readmissions.