Risk estimates of gastrointestinal (GI) diagnoses after an index case of Clostridium difficile.

Study sample baseline characteristics at index admission for Clostridium difficile compared with all Truven hospital admission claims in 2011.

1. This dataset is a de-identified summary table of prevalence rates for vision and eye health data indicators from the 2016 MarketScan® Commercial Claims and Encounters Data (CCAE) is produced by Truven Health Analytics, a division of IBM Watson Health. The CCEA data contain a convenience sample of insurance claims information from person with employer-sponsored insurance and their dependents, including 43.6 million person years of data. Prevalence estimates are stratified by all available combinations of age group, gender, and state. Detailed information on VEHSS MarketScan analyses can be found on the VEHSS MarketScan webpage (cdc.gov/visionhealth/vehss/data/claims/marketscan.html). Information on available Medicare claims data can be found on the IBM MarketScan website (https://marketscan.truvenhealth.com). The VEHSS MarketScan summary dataset was last updated November 2019.

This table provides the data for age and gender comparisons of the three different treatment modalities for keloid management using the Truven Marketscan Insurance claims database. The average treatment length and average number of visits are reported with their respective median and 25th and 75th percentiles.

Purpose: This study evaluated healthcare resource utilization (HCRU), and direct costs among severe aplastic anemia (SAA) patients treated with eltrombopag (EPAG) using US claims data. Methods: This retrospective, real-world claims database study identified SAA patients aged ≥2 years treated with EPAG who initiated any SAA treatment between 1 July 2014 and 31 December 2017 (identification period) using the Truven MarketScan databases. A subset of 82 patients treated with EPAG during the identification period were evaluated for all-cause and SAA-related HCRU and direct costs as well as blood transfusion 1 month before EPAG initiation (baseline) and at Month 6 after EPAG initiation (follow-up period). Results: The average patient age was 50.8 (SD = 20.6) years old, predominantly female (n = 43, 52.4%), and had a mean CCI at baseline of 1.1 (SD = 1.7). Hospitalizations, and ER, office, and outpatient visits were significantly lower at Month 6 after EPAG initiation compared with 1 month before EPAG initiation (p < .05 for all four all-cause HCRU and SAA-related hospitalizations). An almost two-fold decrease in reliance on biweekly blood transfusions was observed: 1.0 at weeks 1–2 to 0.5 at Month 6 after EPAG initiation. Although prescription costs (mean [SD]) were significantly higher at Month 6 after EPAG initiation compared with 1 month before EPAG initiation (difference of $11,045 USD [SD = $18,801]), these increases were offset by savings in direct costs. Overall, a mean reduction in total all-cause costs of $29,391 USD [SD = $137,770] was reported at Month 6 after EPAG initiation due to substantial reductions in hospitalization ($40,060 USD [SD = $123,198]) and outpatient visits ($2,043 USD [SD = $25,264]). Conclusion: All-cause and SAA-related HCRU were reduced following EPAG treatment. Prescription costs were higher following treatment; however, these costs were generally offset by reductions in direct costs. These results provide real-world evidence around the role of EPAG in SAA treatment.

Data Record 1: Drug ingredient combinations: 1-drugdb_drugs_1s.tsvData Record 1: Drug ingredient combinations: 2-drugsSee README.txt for Data Record 1: 1-drugdb_drugs_2s.tsvData Record 1: Drug ingredient combinations: 3-drugsSee README.txt for Data Record 1: 1-drugdb_drugs_3s.tsvData Record 1: Drug ingredient combinations: 4-drugsSee README.txt for Data Record 1: 1-drugdb_drugs_4s.tsvData Record 1: Drug ingredient combinations: 5-drugsSee README.txt for Data Record 1: 1-drugdb_drugs_5s.tsvData Record 2: Drug class combinations: 1-drugSee README.txt for Data Record 1: 1-drugdb_atc_classes_1s.tsvData Record 2: Drug class combinations: 2-drugsSee README.txt for Data Record 1: 1-drugdb_atc_classes_2s.tsvData Record 2: Drug class combinations: 3-drugsSee README.txt for Data Record 1: 1-drugdb_atc_classes_3s.tsvData Record 2: Drug class combinations: 4-drugsSee README.txt for Data Record 1: 1-drugdb_atc_classes_4s.tsvData Record 2: Drug class combinations: 5-drugsSee README.txt for Data Recor...

Baseline characteristics of HF patients stratified by ejection fraction class (HFrEF, < 0.45; or HFpEF, ≥ 0.45).

BackgroundTo the extent that outcomes are mediated through negative perceptions of generics (the nocebo effect), observational studies comparing brand-name and generic drugs are susceptible to bias favoring the brand-name drugs. We used authorized generic (AG) products, which are identical in composition and appearance to brand-name products but are marketed as generics, as a control group to address this bias in an evaluation aiming to compare the effectiveness of generic versus brand medications.Methods and findingsFor commercial health insurance enrollees from the US, administrative claims data were derived from 2 databases: (1) Optum Clinformatics Data Mart (years: 2004–2013) and (2) Truven MarketScan (years: 2003–2015). For a total of 8 drug products, the following groups were compared using a cohort study design: (1) patients switching from brand-name products to AGs versus generics, and patients initiating treatment with AGs versus generics, where AG use proxied brand-name use, addressing negative perception bias, and (2) patients initiating generic versus brand-name products (bias-prone direct comparison) and patients initiating AG versus brand-name products (negative control). Using Cox proportional hazards regression after 1:1 propensity-score matching, we compared a composite cardiovascular endpoint (for amlodipine, amlodipine-benazepril, and quinapril), non-vertebral fracture (for alendronate and calcitonin), psychiatric hospitalization rate (for sertraline and escitalopram), and insulin initiation (for glipizide) between the groups. Inverse variance meta-analytic methods were used to pool adjusted hazard ratios (HRs) for each comparison between the 2 databases. Across 8 products, 2,264,774 matched pairs of patients were included in the comparisons of AGs versus generics. A majority (12 out of 16) of the clinical endpoint estimates showed similar outcomes between AGs and generics. Among the other 4 estimates that did have significantly different outcomes, 3 suggested improved outcomes with generics and 1 favored AGs (patients switching from amlodipine brand-name: HR [95% CI] 0.92 [0.88–0.97]). The comparison between generic and brand-name initiators involved 1,313,161 matched pairs, and no differences in outcomes were noted for alendronate, calcitonin, glipizide, or quinapril. We observed a lower risk of the composite cardiovascular endpoint with generics versus brand-name products for amlodipine and amlodipine-benazepril (HR [95% CI]: 0.91 [0.84–0.99] and 0.84 [0.76–0.94], respectively). For escitalopram and sertraline, we observed higher rates of psychiatric hospitalizations with generics (HR [95% CI]: 1.05 [1.01–1.10] and 1.07 [1.01–1.14], respectively). The negative control comparisons also indicated potentially higher rates of similar magnitude with AG compared to brand-name initiation for escitalopram and sertraline (HR [95% CI]: 1.06 [0.98–1.13] and 1.11 [1.05–1.18], respectively), suggesting that the differences observed between brand and generic users in these outcomes are likely explained by either residual confounding or generic perception bias. Limitations of this study include potential residual confounding due to the unavailability of certain clinical parameters in administrative claims data and the inability to evaluate surrogate outcomes, such as immediate changes in blood pressure, upon switching from brand products to generics.ConclusionsIn this study, we observed that use of generics was associated with comparable clinical outcomes to use of brand-name products. These results could help in promoting educational interventions aimed at increasing patient and provider confidence in the ability of generic medicines to manage chronic diseases.

Comparison of CT use among ED visits and hospitalizations for patients with IBD with pharmaceutical coverage.

Purpose: This study aimed to evaluate the healthcare resource utilization (HCRU) and costs for patients with severe aplastic anemia (SAA) using US claims data. Methods: This retrospective, observational database study analyzed claims data from the Truven MarketScan databases. SAA patients aged ≥2 years identified between 2014 and 2017 who were continuously enrolled for 6 months before their first SAA treatment or blood transfusion, with a ≥6-month follow-up, were included. Baseline demographics and comorbidities were evaluated. Monthly all-cause and SAA-related HCRU and direct costs in the follow-up period were analyzed and differences were presented for all patients and across age groups. Results: With an average follow-up period of 21.5 months, 939 patients were included in the study. Monthly all-cause and SAA-related HCRU [mean (SD)] were 1.65 days (2.61 days) and 0.18 days (0.70 days) for length of stay, 0.18 (0.23) and 0.01 (0.04) for hospital admissions, 0.25 (0.30) and 0.02 (0.07) for ER visits, 2.24 (1.40) and 0.46 (0.99) for office visits, and 2.90 (2.64) and 0.55 (1.31) for outpatient visits, respectively. On average, SAA patients received 0.15 (0.57) blood transfusions per month. Mean monthly all-cause direct costs were $28,280 USD ($36,127) [US dollars, mean (SD)]. Direct costs related to admissions were $11,433 USD (SD $25,040), followed by $624 USD ($1,703) for ER visits, $528 USD ($694) for office visits, $7,615 USD ($13,273) for outpatient visits, and $5,998 USD ($11,461) for pharmacy expenses. Monthly SAA-related direct costs averaged $7,884 USD (SD $16,254); of these costs, $1,608 USD ($7,774) were from admissions, $47 USD ($257) from ER visits, $127 USD ($374) from office visits, $1,462 USD ($4,994) from outpatient visits, and $4,451 USD ($10,552) from pharmacy expenses. Conclusion: SAA is associated with high economic burden, with costs comparable to blood malignancies, implying that US health plans should consider appropriately managing SAA while constraining the total healthcare costs when making formulary decisions.