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      Development of a novel algorithm to determine adherence to chronic pain treatment guidelines using administrative claims

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          Abstract

          Objective

          To develop a claims-based algorithm for identifying patients who are adherent versus nonadherent to published guidelines for chronic pain management.

          Methods

          Using medical and pharmacy health care claims from the MarketScan® Commercial and Medicare Supplemental Databases, patients were selected during July 1, 2010, to June 30, 2012, with the following chronic pain conditions: osteoarthritis (OA), gout (GT), painful diabetic peripheral neuropathy (pDPN), post-herpetic neuralgia (PHN), and fibromyalgia (FM). Patients newly diagnosed with 12 months of continuous medical and pharmacy benefits both before and after initial diagnosis (index date) were categorized as adherent, nonadherent, or unsure according to the guidelines-based algorithm using disease-specific pain medication classes grouped as first-line, later-line, or not recommended. Descriptive and multivariate analyses compared patient outcomes with algorithm-derived categorization endpoints.

          Results

          A total of 441,465 OA patients, 76,361 GT patients, 10,645 pDPN, 4,010 PHN patients, and 150,321 FM patients were included in the development of the algorithm. Patients found adherent to guidelines included 51.1% for OA, 25% for GT, 59.5% for pDPN, 54.9% for PHN, and 33.5% for FM. The majority (~90%) of patients adherent to the guidelines initiated therapy with prescriptions for first-line pain medications written for a minimum of 30 days. Patients found nonadherent to guidelines included 30.7% for OA, 6.8% for GT, 34.9% for pDPN, 23.1% for PHN, and 34.7% for FM.

          Conclusion

          This novel algorithm used real-world pharmacotherapy treatment patterns to evaluate adherence to pain management guidelines in five chronic pain conditions. Findings suggest that one-third to one-half of patients are managed according to guidelines. This method may have valuable applications for health care payers and providers analyzing treatment guideline adherence.

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          Most cited references 22

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          British Society for Rheumatology and British Health Professionals in Rheumatology guideline for the management of gout.

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            A comparison of comorbidity measurements to predict healthcare expenditures.

            To compare the performance of the Elixhauser, Charlson, and RxRisk-V comorbidity indices and several simple count measurements, including counts of prescriptions, physician visits, hospital claims, unique prescription classes, and diagnosis clusters. Each measurement was calculated using claims data during a 1-year period before the initial filling of an antihypertensive medication among 20 378 members of a managed care organization. The primary outcome variable was the log-transformed sum of prescription, physician, and hospital expenditures in the year following the prescription encounter. In addition to descriptive statistics and Spearman rank correlations between measurements, the predictive performance was determined using linear regression models and corresponding adjusted R(2) statistics. The Charlson index and the Elixhauser index performed similarly (adjusted R(2) = 0.1172 and 0.1148, respectively), while the prescription claims-based RxRisk-V (adjusted R(2) = 0.1573) outperformed both. An age- and gender-adjusted regression model that included a count of diagnosis clusters was the best individual predictor of payments (adjusted R(2) = 0.1814). This outperformed age- and gender-adjusted models of the number of unique prescriptions filled (adjusted R(2) = 0.1669), number of prescriptions filled (R(2) = 0.1573), number of physician visits (adjusted R(2) = 0.1546), logtransformed prior healthcare payments (adjusted R(2) = 0.1359), and number of hospital claims (adjusted R(2) = 0.1115). Simple count measurements appear to be better predictors of future expenditures than the comorbidity indices, with a count of diagnosis clusters being the single best predictor of future expenditures among the measurements examined.
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              Medication adherence in patients with chronic non-malignant pain: is there a problem?

              Health care providers, treating patients with chronic non-malignant pain, often experience that medication is not as effective as expected. It is important to realize that the effectiveness of a pharmacological treatment can be influenced by the way the medication is taken. Medication adherence is a topic that gains more attention, especially in chronic conditions, because it affects treatment outcome. A systematic review of studies on medication adherence in patients with chronic non-malignant pain was performed to gain insight in the prevalence of the problem, the impact on treatment outcome, influencing variables and interventions. Searching several electronic databases (Medline, CINAHL, Psychinfo and Cochrane), 14 relevant articles were found. The results indicate that medication non-adherence is common in patients with chronic non-malignant pain. Both overuse and underuse of medication occurs. However, due to the scarce literature and important methodological limitations, it is not possible to make firm conclusions concerning the impact on outcome, influencing variables and optimal intervention strategies. This review highlights some important gaps in the adherence literature in a chronic non-malignant pain population and sets the stage for future research.
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                Author and article information

                Journal
                J Pain Res
                J Pain Res
                Journal of Pain Research
                Journal of Pain Research
                Dove Medical Press
                1178-7090
                2017
                08 February 2017
                : 10
                : 327-339
                Affiliations
                [1 ]Truven Health Analytics, Bethesda, MD
                [2 ]Truven Health Analytics, Cambridge, MA, USA
                [3 ]Pfizer Ltd, Tadworth, UK
                [4 ]Pfizer Inc, Groton, CT
                [5 ]Pfizer Inc, New York, NY, USA
                Author notes
                Correspondence: Jay M Margolis, Truven Health Analytics, 332 Bryn Mawr Avenue, Bala Cynwyd, PA 19004, USA, Email jay.margolis@ 123456truvenhealth.com
                Article
                jpr-10-327
                10.2147/JPR.S118248
                5308584
                © 2017 Margolis et al. This work is published and licensed by Dove Medical Press Limited

                The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License ( http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed.

                Categories
                Original Research

                Anesthesiology & Pain management

                algorithm, chronic pain, adherence, practice guidelines, drug therapy

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