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...

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.

Polypharmacy is increasingly common in the United States, and contributes to the substantial burden of drug-related morbidity. Yet real-world polypharmacy patterns remain poorly characterized. We have counted the incidence of multi-drug combinations observed in four billion patient-months of outpatient prescription drug claims from 2007-2014 in the Truven Health MarketScan® Databases. Prescriptions are grouped into discrete windows of concomitant drug exposure, which are used to count exposure incidences for combinations of up to five drug ingredients or ATC drug classes. Among patients taking any prescription drug, half are exposed to two or more drugs, and 5% are exposed to 8 or more. The most common multi-drug combinations treat manifestations of metabolic syndrome. Patients are exposed to unique drug combinations in 10% of all exposure windows. Our analysis of multi-drug exposure incidences provides a detailed summary of polypharmacy in a large US cohort, which can prioritize common drug combinations for future safety and efficacy studies.

BackgroundMany individuals undergoing cancer treatment experience substantial financial hardship, often referred to as financial toxicity (FT). Those undergoing prostate cancer treatment may experience FT and its impact can exacerbate disparate health outcomes. Localized prostate cancer treatment options include: radiation, surgery, and/or active surveillance. Quality of life tradeoffs and costs differ between treatment options. In this project, our aim was to quantify direct healthcare costs to support patients and clinicians as they discuss prostate cancer treatment options. We provide the transparent steps to estimate healthcare costs associated with treatment for localized prostate cancer among the privately insured population using a large claims dataset.MethodsTo quantify the costs associated with their prostate cancer treatment, we used data from the Truven Health Analytics MarketScan Commercial Claims and Encounters, including MarketScan Medicaid, and peer reviewed literature. Strategies to estimate costs included: (1) identifying the problem, (2) engaging a multidisciplinary team, (3) reviewing the literature and identifying the database, (4) identifying outcomes, (5) defining the cohort, and (6) designing the analytic plan. The costs consist of patient, clinician, and system/facility costs, at 1-year, 3-years, and 5-years following diagnosis.ResultsWe outline our specific strategies to estimate costs, including: defining complex research questions, defining the study population, defining initial prostate cancer treatment, linking facility and provider level related costs, and developing a shared understanding of definitions on our research team.Discussion and next stepsAnalyses are underway. We plan to include these costs in a prostate cancer patient decision aid alongside other clinical tradeoffs.

Background: For Parkinson's disease (PD), essential tremor (ET), and dystonia patients with deep brain stimulation (DBS) implants, magnetic resonance imaging (MRI) requires additional safety considerations due to potentially hazardous interactions. Objective: A propensity-matched cohort of DBS-implanted patients was analyzed to determine the likelihood of needing MRI. Methods: Patients with new DBS full-system implants (n = 576) were identified in the Truven Health MarketScan® Commercial Claims and Medicare Supplemental Databases (2009-2012). Patients diagnosed with PD, ET, or dystonia and no DBS implant were identified (DBS-indicated patients: n = 11,216). The DBS-indicated patients were continuously enrolled for 4 years and matched for age, gender, and propensity score based on comorbid conditions to DBS-implanted patients (n = 4,878 and 543, respectively). A Kaplan-Meier survival curve of time to first MRI was extrapolated to 10 years. Results: An estimated 56-57% of DBS-indicated patients need an MRI within 5 years and 66-75% within 10 years after implantation. While 92% of DBS-implanted patients' MRI after implantation was of the head, for DBS-indicated patients, 62% of MRIs were of the body, potentially unrelated to the primary diagnosis. Conclusions: This analysis highlights the projected utilization of MRI in the DBS population for head and full-body images.

Access to highly specialized interventional oncology procedures such as transarterial chemoembolization (TACE) and radioembolization (TARE) may be limited in non-metropolitan areas of the United States. This study characterizes the distribution of these procedures across regions by metropolitan status through utilization of a large commercial healthcare claims database (Truven Merative Marketscan). Patients with a diagnosis of primary hepatocellular carcinoma (HCC) (n= 41,280) were categorized into those who received TACE (n = 1,780) or TARE (n = 1,179). Chi-squared tests of association were utilized to analyze regional data. Statistical analyses showed significant differences between most regional comparisons with most patients receiving these procedures originating from metropolitan areas overall. Though limited to TACE and TARE, this study reveals a disparate distribution of TACE and TARE utilization across regions with preference towards metropolitan over non-metropolitan areas, which may represent a barrier for access to care for nonmetropolitan patients, though this remains to be studied.

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.

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.