Comparison of Specialty Medication Use for Common Chronic Inflammatory Diseases Among Health Exchange and Other Commercially Insured Members.

BACKGROUND: The Affordable Care Act of 2010 allows the purchase of health insurance through special marketplaces called "health exchanges." The majority of individuals enrolling in the exchanges were previously uninsured, older, and sicker than other commercially insured members. Early evidence also suggests that exchange plan members use more costly specialty drugs compared with other commercially insured members. OBJECTIVES: To (a) examine patient characteristics and specialty drug use for common chronic inflammatory diseases (CIDs) among exchange plan members compared with other commercially insured members and (b) explore variations in specialty drug use within exchange plans by metal tiers (bronze, silver, gold, and platinum), as well as across local markets. METHODS: This analysis included adults aged ≥ 18 years who were enrolled in exchange plans (exchange population) and other commercial health plans (nonexchange population). The primary outcome was the likelihood of using specialty drugs prescribed to treat common CIDs, such as rheumatoid arthritis, ankylosing spondylitis, Crohn's disease, ulcerative colitis, psoriatic arthritis, and psoriasis. The adjusted likelihood of using CID specialty drugs was calculated from logistic regression controlling for prevalence of CIDs and other health risk factors. RESULTS: A total of 931,384 exchange plan members and 2,682,855 nonexchange plan members were included in the analysis. Compared with the nonexchange population, the exchange population was older, more likely to be female, had more comorbid conditions, but filled fewer prescriptions. The 2 groups were similar in terms of CID prevalence. The observed likelihood of CID specialty drug use was 20.0% lower in the exchange versus the nonexchange populations (341 users per 100,000 exchange members vs. 427 users per 100,000 nonexchange members; P < 0.001). Within the exchange population, the observed likelihood of CID specialty drug use was 132 per 100,000 bronze plan members (69.1% lower than nonexchange); 326 per 100,000 silver plan members (23.5% lower than nonexchange); 579 per 100,000 gold plan members (35.6% higher than nonexchange); and 672 per 100,000 platinum plan members (57.5% higher than nonexchange). All differences were statistically significant at P < 0.001. There were also large differences by local market, ranging from 49.1% lower to 75.8% higher CID use in the exchange population than in the nonexchange population. After adjustment, the exchange population was 16.6% less likely to use CID specialty drugs than the nonexchange population (P < 0.001). Large variation in specialty drug use within the exchange plan metal tiers was reduced. After adjustment, the higher use of CID specialty drugs among the exchange population in certain local plans was no longer statistically significant. CONCLUSIONS: Members insured through exchange plans were older and sicker than those with nonexchange plans, but they did not use more CID specialty drugs compared with the nonexchange population. Large variations were seen among the exchange plan metal tiers and by local markets, which were often related to the risk profiles of exchange plan enrollees. DISCLOSURES: Funding for this study was provided by Anthem. Anthem had no role in study design, data interpretation, manuscript development, or the decision to publish. Chen, Gautam, DeVries, and Sylwestrzak are employees of HealthCore, a wholly owned subsidiary of Anthem. Richards is an employee of Anthem. Ruggieri is a former employee of Anthem and a current employee of MedImpact Healthcare Systems. Study concept and design were contributed by Ruggieri, Richards, DeVries, and Sylwestrzak. Chen took the lead in data collection, along with Gautam. Data interpretation was performed by Chen, along with the other authors. The manuscript was written by Chen, Gautam, Sylwestrzak, and DeVries and revised by Chen, Gautam, and Sylwestrzak, along with the other authors.

Intravenous Versus Subcutaneous Anti-TNF-Alpha Agents for Crohn's Disease: A Comparison of Effectiveness and Safety.

BACKGROUND: In recent years, there have been a number of pharmacological innovations for Crohn's disease (CD), a difficult-to-treat condition, including new treatment philosophies (e.g., top-down therapy) and new therapeutic options in terms of the agent and the route of administration. Three anti-tumor necrosis factor (anti-TNF-alpha) agents are available for use among CD patients in the United States: infliximab, an intravenous agent, and adalimumab and certolizumab pegol, 2 newer subcutaneous products. Infliximab is considered the "gold standard" because it has the longest clinical experience, and adalimumab and certolizumab pegol have each gained significant market share. OBJECTIVE: To examine differences in effectiveness and safety between currently available intravenous and subcutaneous anti-TNF-alpha agents used to treat patients with CD. METHODS: Data for this retrospective, administrative claims analysis were obtained from pharmacy and medical claims from major U.S. health plans geographically dispersed across 14 states during 2007-2011. Patients had at least 1 ICD-9-CM diagnosis for CD, 6 months pre-index eligibility, and initiated anti-TNF-alpha therapy on the index date. Patients in each cohort were propensity score matched on pre-index demographics, clinical characteristics, and baseline health care use. During the post-index period, age-sex adjusted incidence rate ratios (IRRs) of CD-related symptoms, infections, cancers, and hepatic-related conditions were compared using Cox (PH) models. RESULTS: The matched cohorts included 515 patients in each group, with an average age of 39 years. Median follow-up was 17.5 months in the intravenous cohort and 17.7 months in the subcutaneous cohort. In terms of effectiveness outcomes, age-sex adjusted IRRs for the subcutaneous group, with the intravenous cohort as a reference, were as follows: 0.61 (95% CI = 0.32-1.18, P = 0.14) for anal fissures; 0.97 (95% CI = 0.72-1.30, P = 0.85) for abscess; 1.08 (95% CI = 0.79-1.04, P = 0.64) for fistulas; 1.12 (95% CI = 0.83-1.54, P = 0.45) for gastrointestinal hemorrhage; and 1.22 (95% CI = 0.93-1.59, P = 0.14) for a combined measure of obstruction, occlusion, stenosis, and stricture of intestine. In terms of safety outcomes, age-sex adjusted IRRs for the subcutaneous group were as follows: 0.85 (95% CI = 0.62-1.16, P = 0.30) for infections; 1.16 (95% CI = 0.71-1.89, P = 0.55) for cancers; and 1.23 (95% CI = 0.79-1.92, P = 0.35) for hepatic-related conditions. CONCLUSIONS: After adjusting for baseline characteristics, effectiveness and safety outcomes appear to be comparable between intravenous and subcutaneous anti-TNF-alpha agents in patients with CD. With similar outcomes, other considerations such as convenience of administration and patient preference may play a more prominent role in choice of agent. Health care providers and health payers should inform CD patients about the range of options available when selecting an anti-TNF-alpha agent.

Considering patient preferences when selecting anti-tumor necrosis factor therapeutic options.

BACKGROUND: Anti-tumor necrosis factor (TNF) medications for the treatment of chronic inflammatory conditions represent a large and growing expenditure for health plans. Over the past few years, there has been an increase in options for patients receiving anti-TNFs, including choice of agent, route of administration, and location for receiving the medication. OBJECTIVE: To examine patient preferences regarding available anti-TNF agents and mode of administration options. METHODS: This cross-sectional survey and claims study was based on administrative claims in the HealthCore Integrated Research Database. Patients were identified for this study if they were receiving infliximab (the intravenous [IV] group) or adalimumab, golimumab, etanercept, or certolizumab pegol (the subcutaneous [SC] group) between March 2012 and August 2012 and were diagnosed with conditions for which these agents are indicated by the US Food and Drug Administration. The survey questionnaire was developed specifically for this study. Participants were asked about their use of anti-TNF agents, locations of administration, preferences for IV or SC therapy, interest in anti-TNF home therapy options, and their physician's role in their decision-making process. A validated instrument, the Treatment Satisfaction Questionnaire for Medication (TSQM) version II, was used to assess treatment satisfaction by the patients. RESULTS: A total of 6000 patients were included in the final list of patients, and the study was stopped when the targeted number of 500 surveys were completed. The IV group consisted of 202 (40%) patients, and the SC group consisted of 298 (60%) patients. Patients in the SC group had a higher preference for the administration route they were using compared with patients in the IV group: 89.9% of the SC group preferred the SC route of administration, whereas 71.8% of the IV group preferred the IV route (P <.001). The global treatment satisfaction scores were similar in both groups (81.9 in the IV group, 80.1 in the SC group; P = .247). The reported likelihood of patients discussing alternative anti-TNF options with their physician was low (45.5% in the IV group vs 49.7% in the SC group; P = .366). CONCLUSIONS: When asked to make a hypothetical choice between IV and SC administration, patients had stronger preferences for SC routes than for IV routes. There was a strong correlation between the route of administration in use and the preference, indicating high level of satisfaction with the current treatment used, which was confirmed with the TSQM version II results. An opportunity for patient education exists, because conversations with physicians about alternative anti-TNF therapies and administration appear to be lacking.

Towards a framework for developing semantic relatedness reference standards.

Our objective is to develop a framework for creating reference standards for functional testing of computerized measures of semantic relatedness. Currently, research on computerized approaches to semantic relatedness between biomedical concepts relies on reference standards created for specific purposes using a variety of methods for their analysis. In most cases, these reference standards are not publicly available and the published information provided in manuscripts that evaluate computerized semantic relatedness measurement approaches is not sufficient to reproduce the results. Our proposed framework is based on the experiences of medical informatics and computational linguistics communities and addresses practical and theoretical issues with creating reference standards for semantic relatedness. We demonstrate the use of the framework on a pilot set of 101 medical term pairs rated for semantic relatedness by 13 medical coding experts. While the reliability of this particular reference standard is in the "moderate" range; we show that using clustering and factor analyses offers a data-driven approach to finding systematic differences among raters and identifying groups of potential outliers. We test two ontology-based measures of relatedness and provide both the reference standard containing individual ratings and the R program used to analyze the ratings as open-source. Currently, these resources are intended to be used to reproduce and compare results of studies involving computerized measures of semantic relatedness. Our framework may be extended to the development of reference standards in other research areas in medical informatics including automatic classification, information retrieval from medical records and vocabulary/ontology development.

A corpus driven approach applying the "frame semantic" method for modeling functional status terminology.

In an effort to unearth semantic models that could prove fruitful to functional-status terminology development we applied the "frame semantic" method, derived from the linguistic theory of thematic roles currently exemplified in the Berkeley "FrameNet" Project. Full descriptive sentences with functional-status conceptual meaning were derived from structured content within a corpus of questionnaire assessment instruments commonly used in clinical practice for functional-status assessment. Syntactic components in those sentences were delineated through manual annotation and mark-up. The annotated syntactic constituents were tagged as frame elements according to their semantic role within the context of the derived functional-status expression. Through this process generalizable "semantic frames" were elaborated with recurring "frame elements". The "frame semantic" method as an approach to rendering semantic models for functional-status terminology development and its use as a basis for machine recognition of functional status data in clinical narratives are discussed.

From clinical records to regulatory reporting: formal terminologies as foundation.

Achieving goals of healthy people and populations is dependent on available and relevant data for health care decisions. New technologies enable reuse of data for decisions, however it is clear that uniform data standards and in particular standards around terminological data will be required to achieve reuse. Terminological data related to functioning and disability presents unique challenges because of the conceptual ambiguity within the field. The International Classification of Functioning, Disability, and Health (ICF) provides a clarifying conceptual foundation for functioning and disability data, but is not structured as a formal terminology. The need for a concerted and coordinated effort is emphasized.

Maximum entropy modeling for mining patient medication status from free text.

Using a classification scheme of patient medication status we sought to recognize and categorize medications mentioned in the unrestricted text of clinical documents generated in clinical practice. The categories refer to the patient's status with respect to the medication such as discontinuation, start or initiation, and continuation of a given medication. This categorization is performed with a machine learning technique, Maximum Entropy (ME), that is well suited to incorporating heterogeneous sources of information necessary for classifying patient's medication status. We use hand labeled training data to generate ME models and test 5 different training feature sets. Our results show that the most optimal feature set includes a combination of the following: two words preceding and following the mention of the drug, the subject of the sentence in which the drug mention occurs, the 2 words following the subject, and a binary feature vector of lexicalized semantic cues indicative of medication status or its change. The average predictive power of a model trained on these features is approximately 89%.