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

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.

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

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

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.

Univariable predictors of CT use during an Inpatient or ED visit with a first or second diagnosis of IBD.

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...

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

ObjectivesPatients with inflammatory bowel disease(IBD) are frequently exposed to computed tomography (CT). Each CT exposes patients to radiation that cumulatively could increase the risk of malignancy, particularly in younger patients. We aim to study the effect of age on CT use in IBD patients seen in the Emergency Department (ED) or the hospital.MethodsWe conducted a retrospective cohort study of IBD patients identified in Truven Health Marketscan databases between 2009–2013. The main outcome was use of CT during an ED or inpatient visit. Effect of age on CT use was characterized using logistic regression accounting for important covariables.ResultsThere were 66,731 patients with IBD with 144,147 ED or inpatient visits in this cohort with a diagnosis code of IBD. At first visit, 5.8% percent were below age 18. CT was utilized in 26.6% of visits. In multivariable analysis, adjusting for medications, recent surgery, and gender, patients 18–35 were more likely to undergo CT (OR 2.35, 95%CI: 2.20–2.52) compared to those

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.

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.

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.

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.

Administrative claims and electronic health records are valuable resources for evaluating pharmaceutical effects during pregnancy. However, direct measures of gestational age are generally not available. Establishing a reliable approach to infer the duration and outcome of a pregnancy could improve pharmacovigilance activities. We developed and applied an algorithm to define pregnancy episodes in four observational databases: three US-based claims databases: Truven MarketScan® Commercial Claims and Encounters (CCAE), Truven MarketScan® Multi-state Medicaid (MDCD), and the Optum ClinFormatics® (Optum) database and one non-US database, the United Kingdom (UK) based Clinical Practice Research Datalink (CPRD). Pregnancy outcomes were classified as live births, stillbirths, abortions and ectopic pregnancies. Start dates were estimated using a derived hierarchy of available pregnancy markers, including records such as last menstrual period and nuchal ultrasound dates. Validation included clinical adjudication of 700 electronic Optum and CPRD pregnancy episode profiles to assess the operating characteristics of the algorithm, and a comparison of the algorithm’s Optum pregnancy start estimates to starts based on dates of assisted conception procedures. Distributions of pregnancy outcome types were similar across all four data sources and pregnancy episode lengths found were as expected for all outcomes, excepting term lengths in episodes that used amenorrhea and urine pregnancy tests for start estimation. Validation survey results found highest agreement between reviewer chosen and algorithm operating characteristics for questions assessing pregnancy status and accuracy of outcome category with 99–100% agreement for Optum and CPRD. Outcome date agreement within seven days in either direction ranged from 95–100%, while start date agreement within seven days in either direction ranged from 90–97%. In Optum validation sensitivity analysis, a total of 73% of algorithm estimated starts for live births were in agreement with fertility procedure estimated starts within two weeks in either direction; ectopic pregnancy 77%, stillbirth 47%, and abortion 36%. An algorithm to infer live birth and ectopic pregnancy episodes and outcomes can be applied to multiple observational databases with acceptable accuracy for further epidemiologic research. Less accuracy was found for start date estimations in stillbirth and abortion outcomes in our sensitivity analysis, which may be expected given the nature of the outcomes.

Objective: The objective was to characterize psoriasis treatment patterns, including estimating persistence and describing subsequent events (i.e. switching and restarting) for all systemic therapies. Methods: This retrospective cohort study utilized Truven MarketScan databases from 1 January 2014 to 31 December 2016 to investigate persistence, switching and restarting in new users of systemic psoriasis medications. Descriptive statistics, time-to-event analyses and a Cox proportional hazards regression were conducted. Results: A total of 5205 patients met inclusion criteria. Regardless of treatment type, >50% lost persistence by 12 months. Patients newly initiating acitretin or non-TNF biologic experienced the highest loss of persistence (85.2%, 73.8%, respectively). Patients initiating a TNF-α inhibitor or apremilast experienced the lowest loss (51.8%, 56.4% respectively). Treatment type had a statistically significant effect on persistence loss (adjusted hazard ratio: 0.86, 95% CI: 0.81, 0.91). Restarting was the most commonly observed event for patients on an oral or biologic (60.2%, 79.9%, respectively). The most common switch from an oral was to a TNF-α inhibitor, while apremilast often followed biologics. Conclusion: Most patients lost persistence on initial treatment by 12 months, and the majority restarted treatment. This may indicate poor compliance or the cyclical nature of psoriasis. More patients switched from an oral to biologic than vice versa, likely due to formulary design and preference for orals. Studies are needed to investigate underlying reasons and patient characteristics that differentiate treatment utilization.

Background/Aims: Few studies explore the magnitude of the disease burden and health care utilization imposed by renal disease among patients with hepatitis C virus (HCV). We aimed to describe the characteristics, outcomes, and health care utilization and costs of patients with HCV with and without renal impairment. Methods: This retrospective analysis used 2 administrative claims databases: the US commercially insured population in Truven Health MarketScan® data (aged 20-64 years), and the US Medicare fee-for-service population in the Medicare 20% sample (aged ≥65 years). Baseline characteristics and comorbid conditions were identified from claims during 2011; patients were followed for up to 1 year (beginning January 1, 2012) to identify health outcomes of interest and health care utilization and costs. Results: In the MarketScan and Medicare databases, 35,965 and 10,608 patients with HCV were identified, 8.5 and 26.5% with evidence of renal disease (chronic kidney disease [CKD] or end-stage renal disease [ESRD]). Most comorbid conditions and unadjusted outcome rates increased across groups from patients with no evidence of renal disease to non-ESRD CKD to ESRD. Health care utilization followed a similar pattern, as did the costs. Conclusions: Our findings suggest that HCV patients with concurrent renal disease have significantly more comorbidity, a higher likelihood of negative health outcomes, and higher health care utilization and costs.

BackgroundThe rise in health spending in the United States and the prevalence of multimorbidity—having more than one chronic condition—are interlinked but not well understood. Multimorbidity is believed to have an impact on an individual’s health spending, but how having one specific additional condition impacts spending is not well established. Moreover, most studies estimating spending for single diseases rarely adjust for multimorbidity. Having more accurate estimates of spending associated with each disease and different combinations could aid policymakers in designing prevention policies to more effectively reduce national health spending. This study explores the relationship between multimorbidity and spending from two distinct perspectives: (1) quantifying spending on different disease combinations; and (2) assessing how spending on a single diseases changes when we consider the contribution of multimorbidity (i.e., additional/reduced spending that could be attributed in the presence of other chronic conditions).Methods and findingsWe used data on private claims from Truven Health MarketScan Research Database, with 16,288,894 unique enrollees ages 18 to 64 from the US, and their annual inpatient and outpatient diagnoses and spending from 2018. We selected conditions that have an average duration of greater than one year among all Global Burden of Disease causes. We used penalized linear regression with stochastic gradient descent approach to assess relationship between spending and multimorbidity, including all possible disease combinations with two or three different conditions (dyads and triads) and for each condition after multimorbidity adjustment. We decomposed the change in multimorbidity-adjusted spending by the type of combination (single, dyads, and triads) and multimorbidity disease category.We defined 63 chronic conditions and observed that 56.2% of the study population had at least two chronic conditions. Approximately 60.1% of disease combinations had super-additive spending (e.g., spending for the combination was significantly greater than the sum of the individual diseases), 15.7% had additive spending, and 23.6% had sub-additive spending (e.g., spending for the combination was significantly less than the sum of the individual diseases). Relatively frequent disease combinations (higher observed prevalence) with high estimated spending were combinations that included endocrine, metabolic, blood, and immune disorders (EMBI disorders), chronic kidney disease, anemias, and blood cancers. When looking at multimorbidity-adjusted spending for single diseases, the following had the highest spending per treated patient and were among those with high observed prevalence: chronic kidney disease ($14,376 [12,291,16,670]), cirrhosis ($6,465 [6,090,6,930]), ischemic heart disease (IHD)-related heart conditions ($6,029 [5,529,6,529]), and inflammatory bowel disease ($4,697 [4,594,4,813]). Relative to unadjusted single-disease spending estimates, 50 conditions had higher spending after adjusting for multimorbidity, 7 had less than 5% difference, and 6 had lower spending after adjustment.ConclusionsWe consistently found chronic kidney disease and IHD to be associated with high spending per treated case, high observed prevalence, and contributing the most to spending when in combination with other chronic conditions. In the midst of a surging health spending globally, and especially in the US, pinpointing high-prevalence, high-spending conditions and disease combinations, as especially conditions that are associated with larger super-additive spending, could help policymakers, insurers, and providers prioritize and design interventions to improve treatment effectiveness and reduce spending.