Rheumatoid arthritis (RA) is a chronic debilitating disease affecting 1% of the adult US population. It is one of the most demanding diseases on our healthcare resources. Although the treatment costs of RA have recently increased, most of the economic impact is due to consequences of RA rather than direct treatment costs. The indirect cost (e.g., due to productivity loss) is estimated to be two to three times higher than the direct treatment costs. 1) Biologics have recently been introduced in the treatment of RA. These drugs target the inflammatory mediators in RA. Biologics have proven to be effective in slowing the progression of the joint damage and are well tolerated by RA patients. However, biologics are one of the most expensive specialty drugs. Not every RA patient can afford them. In fact, drug affordability in the most recent American College of Rheumatology's (ACR) guideline limits biologics use. 2) In these guidelines biologics are only offered if the patient can afford them either privately or through health insurance coverage. 3) Current comparative effectiveness research (CER) may not address the potential long-term benefits of biologics. This evidence is primarily based on short-term randomized clinical trials (RCT) that compare biologics to placebos. Short-term comparative effectiveness evidence shows that biologics are more effective than non-biologics, but they are only marginally cost-effective. If biologics also prove to be more effective in the long run, they may be more cost-effective than initially thought. 3) incorporating long-term treatment sequences from the National Databank of Rheumatic Diseases (NDB) is a major proposed improvement in this study. Medication sequences can play a major role in maximizing patient benefits because RA patients often switch medications. Prior studies often ignored long-term medication sequences because (1) such sequences are not observed in clinical trials, and (2) these sequences are impractical to model using previous methods. This analysis proposes to: (1) use Markov Decision Processes (MDP) to efficiently model a large number of RA treatment sequences, (2) generate comparative effectiveness evidence from the NDB, and (3) identify the best treatment sequences for specific categories of patients determined by characteristics such as age, gender and comorbidities. This research addresses the Agency for Healthcare Research and Quality's (AHRQ) mission by conducting comparative effectiveness research. Comparative effectiveness of biologics in RA is recognized as one of the highest priorities of the National Institute of Health (NIH) in the US. The results from this analysis have potential policy and clinical implications. First, medical insurance coverage can be updated to suggest treatment choices that are best for RA patients and that lower societal costs. Second, this proposed research can identify treatment sequences tailored to specific categories of patients. Finally, this research addresses Health Services Research issues critical to the AHRQ priority populations. RA is most prevalent among women and the elderly. In addition, RA patients are often disabled or require chronic healthcare.

Public Health Relevance

This project seeks to identify rheumatoid arthritis (RA) treatment sequences that are best for RA patients and can lower healthcare costs. Identifying these sequences from real-life clinical practice data is important to provide much needed long-term comparative effectiveness evidence in order to reshape policies and clinical guidelines. This research improves upon prior studies by using information from real-life RA treatment data and incorporating sequential medication use.

National Institute of Health (NIH)
Agency for Healthcare Research and Quality (AHRQ)
Dissertation Award (R36)
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HSR Health Care Research Training SS (HCRT)
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Harding, Brenda
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University of Minnesota Twin Cities
Public Health & Prev Medicine
Schools of Public Health
United States
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Jalal, Hawre; Dowd, Bryan; Sainfort, Fran├žois et al. (2013) Linear regression metamodeling as a tool to summarize and present simulation model results. Med Decis Making 33:880-90