Duloxetine use in chronic low back pain: treatment patterns and costs.
ABSTRACT Little is known about the real-world treatment patterns and costs of patients with chronic low back pain (CLBP) who are treated with duloxetine compared with those receiving other non-surgical treatments.
Our objective was to compare the real-world treatment patterns and costs between patients with CLBP who initiated duloxetine and matched controls who initiated another non-surgical treatment.
The study sample was selected from a US privately insured claims database (2004-8). Selected patients were aged 18-64 years, and had a low back pain (LBP) diagnosis (per Healthcare Effectiveness Data and Information Set [HEDIS] specifications) with a subsequent CLBP-qualifying diagnosis recorded ≥90 days after the initial LBP diagnosis. Duloxetine-treated patients had ≥1 duloxetine prescription within 6 months after CLBP diagnosis, no prior duloxetine claim, and continuous eligibility ≥12 months before first LBP diagnosis and ≥6 months after index duloxetine prescription (study period). Because duloxetine patients had higher rates of co-morbidities, 553 duloxetine-treated patients were matched to 553 control patients who initiated another non-surgical LBP treatment based on propensity score and time from first LBP diagnosis to treatment initiation. A subset (n = 103 each) of matched employees with disability data was also analysed to assess work loss. Main outcomes measures included study period treatment rates and direct (medical and drug) costs from a third-party payer perspective and employee indirect (work-loss) costs. McNemar tests were used to compare LBP treatment rates. Bias-corrected bootstrapping t-tests were used to compare costs.
After matching, the two groups had balanced baseline characteristics including demographics, LBP diagnostic categories, co-morbidity profiles, resource use, treatment patterns and mean direct costs. During the 6-month study period, matched duloxetine-treated patients had significantly lower rates of other pharmacological therapy (e.g. 56.2% vs 64.9% narcotic opioids, p = 0.0024; 34.9% vs 49.5% NSAIDs, p < 0.0001) and non-invasive therapy (28.8% vs 38.5% chiropractic therapy, p = 0.0007; 25.5% vs 35.4% physical therapy, p = 0.0004; 17.5% vs 28.4% exercise therapy, p < 0.0001) than controls. Duloxetine-treated patients versus controls had similar back surgery rates (2.2% vs 3.8%; p = 0.1127) and similar direct costs ($US7658 vs $US7439; p = 0.8119). Among CLBP employees, duloxetine-treated employees versus controls had lower rates of other non-surgical therapy, similar back surgery rates (0.0% vs 3.9%; p = 0.1250), lower total direct and indirect costs ($US5227 vs $US7299; p = 0.0418), and similar indirect costs ($US1806 vs $US2664; p = 0.0528).
Duloxetine treatment in CLBP patients/employees versus other non-surgical treatment was associated with reduced rates of non-surgical therapies and similar back surgery rates, without increased costs.
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ABSTRACT: To assess the cost-effectiveness of duloxetine in the treatment of chronic low back pain (CLBP) from a US private payer perspective. A cost-utility analysis was undertaken for duloxetine and seven oral post-first-line comparators, including nonsteroidal anti-inflammatory drugs (NSAIDs), weak and strong opioids, and an anticonvulsant. We created a Markov model on the basis of the National Institute for Health and Clinical Excellence model documented in its 2008 osteoarthritis clinical guidelines. Health states included treatment, death, and 12 states associated with serious adverse events (AEs). We estimated treatment-specific utilities by carrying out a meta-analysis of pain scores from CLBP clinical trials and developing a transfer-to-utility equation using duloxetine CLBP patient-level data. Probabilities of AEs were taken from the National Institute for Health and Clinical Excellence model or estimated from osteoarthritis clinical trials by using a novel maximum-likelihood simulation technique. Costs were gathered from Red Book, Agency for Healthcare Research and Quality's Healthcare Cost and Utilization Project database, the literature, and, for a limited number of inputs, expert opinion. The model performed one-way and probabilistic sensitivity analyses and generated incremental cost-effectiveness ratios (ICERs) and cost acceptability curves. The model estimated an ICER of $59,473 for duloxetine over naproxen. ICERs under $30,000 were estimated for duloxetine over non-NSAIDs, with duloxetine dominating all strong opioids. In subpopulations at a higher risk of NSAID-related AEs, the ICER over naproxen was $33,105 or lower. Duloxetine appears to be a cost-effective post-first-line treatment for CLBP compared with all but generic NSAIDs. In subpopulations at risk of NSAID-related AEs, it is particularly cost-effective.Value in Health 03/2013; 16(2):334-44. DOI:10.1016/j.jval.2012.12.006 · 2.89 Impact Factor
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ABSTRACT: ABSTRACT Outcomes research studies use clinical and administrative data generated in the course of patient care or from patient surveys to examine the effectiveness of treatments. Health care providers need to understand the limitations and strengths of the real-world data sources used in outcomes studies to meaningfully use the results. This paper describes five types of databases commonly used in the United States for outcomes research studies, discusses their strengths and limitations, and provides examples of each within the context of pain treatment. The databases specifically discussed are generated from (1) electronic medical records, which are created from patient-provider interactions; (2) administrative claims, which are generated from providers' and patients' transactions with payers; (3) integrated health systems, which are generated by systems that provide both clinical care and insurance benefits and typically represent a combination of electronic medical record and claims data; (4) national surveys, which provide patient-reported responses about their health and behaviors; and (5) patient registries, which are developed to track patients with a given disease or exposure over time for specified purposes, such as population management, safety monitoring, or research.Journal of Pain & Palliative Care Pharmacotherapy 08/2014; 28(3). DOI:10.3109/15360288.2014.941131
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ABSTRACT: This study applied a uniform methodology for measuring and comparing duloxetine adherence in the treatment of multiple chronic medical conditions. Study patients 18-64 years of age initiating duloxetine therapy during 2008 were identified from a large managed care database. The study was restricted to patients with continuous health plan eligibility for 12 months pre- and post-duloxetine initiation. Study patients had ≥1 medical claim with an inpatient or outpatient diagnosis of one (and only one) of the following conditions: major depressive disorder (MDD); generalized anxiety disorder (GAD); fibromyalgia, diabetic peripheral neuropathic pain; or chronic musculoskeletal pain, as established in studies in patients with osteoarthritis and chronic lower back pain (CLBP). Patients initiating duloxetine who had two or more of the six studied conditions were not included in this study, thereby avoiding the need to differentiate between primary and secondary diagnoses from the claims records. Adherence rate was defined as the percentage of patients with a 365-day medication possession ratio ≥0.8. A total of 20,490 patients initiated duloxetine treatment during 2008 with a diagnosis of one of the studied conditions during the study period. The adherence rate in our sample was 34.6% and was highest among patients with MDD (37.3%) and lowest for patients with CLBP (29.9%). In general, adherence among patients with MDD and GAD was greater than among those with a chronic pain condition. Adherence among newly initiated duloxetine patients varied modestly across the medical conditions for which it was used. After adjusting for potential confounders, differences between the mental conditions (MDD and GAD) and the chronic pain conditions (CLBP, osteoarthritis, and diabetic peripheral neuropathic pain) were statistically significant. These results may be useful in the determination of expectations of adherence, and how it may differ for each of the conditions studied.ClinicoEconomics and Outcomes Research 01/2014; 6:75-81. DOI:10.2147/CEOR.S52950