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      Patient Preference and Adherence (submit here)

      This international, peer-reviewed Open Access journal by Dove Medical Press focuses on the growing importance of patient preference and adherence throughout the therapeutic process. Sign up for email alerts here.

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      Comparative Adherence Trajectories of Oral Fingolimod and Injectable Disease Modifying Agents in Multiple Sclerosis

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          Abstract

          Background

          Oral fingolimod is convenient to use than injectable disease modifying agents (DMAs) in patients with multiple sclerosis (MS). However, the existing literature regarding the comparative adherence trajectories between oral fingolimod and injectable DMAs is limited.

          Objective

          To compare the adherence trajectories between oral DMA, fingolimod, and injectable DMAs in patients with MS.

          Methods

          A retrospective longitudinal study was conducted using adults (≥18 years) with MS (ICD-9-CM: 340 and a DMA prescription) from the IBM MarketScan Commercial Claims and Encounters Database between 2010 and 2012. Patients were grouped into oral fingolimod or injectable DMA users based on the index DMA among patients with MS. The annual DMA adherence trajectories, based on the proportion of days covered (PDC), were examined using group-based trajectory modeling (GBTM) during the one-year follow-up period after treatment initiation. Multivariable multinomial logistic regression using stabilized inverse probability treatment weights (IPTW) was performed to assess the association between the DMA route of administration (Oral vs Injectable) and the adherence trajectory groups. The balance of covariates between oral and injectable DMAs before and after IPTW was checked against a standardized difference threshold of 0.25.

          Results

          The study cohort consisted of 1,700 MS patients who were initiated with oral (15.8%) or injectable (84.2%) DMAs between 2010 and 2012. The adherence rates (PDC≥0.8) in oral fingolimod and injectable DMA users were found to be 64.7% and 50.8%, respectively. The GBTM grouped individuals in the study cohort into three adherence trajectories – rapid discontinuers (23.5%), complete adherers (49.9%), and slow decliners (26.6%). The multinomial logistic regression model with stabilized IPTW revealed that oral fingolimod users had higher odds to be a complete adherer (adjusted odds ratio [AOR]: 2.78, 95% CI: 1.85–4.16) or a slow discontinuer (AOR: 2.62, 95% CI: 1.70–4.05) than injectable DMA users.

          Conclusions

          Oral DMA fingolimod was associated with better adherence than injectable DMAs across group-based trajectories. Further research is warranted to evaluate the adherence trajectories with newer oral DMAs introduced in the last decade for MS.

          Most cited references47

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          Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.

          Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this, we conducted a multistep process to develop ICD-10 coding algorithms to define Charlson and Elixhauser comorbidities in administrative data and assess the performance of the resulting algorithms. ICD-10 coding algorithms were developed by "translation" of the ICD-9-CM codes constituting Deyo's (for Charlson comorbidities) and Elixhauser's coding algorithms and by physicians' assessment of the face-validity of selected ICD-10 codes. The process of carefully developing ICD-10 algorithms also produced modified and enhanced ICD-9-CM coding algorithms for the Charlson and Elixhauser comorbidities. We then used data on in-patients aged 18 years and older in ICD-9-CM and ICD-10 administrative hospital discharge data from a Canadian health region to assess the comorbidity frequencies and mortality prediction achieved by the original ICD-9-CM algorithms, the enhanced ICD-9-CM algorithms, and the new ICD-10 coding algorithms. Among 56,585 patients in the ICD-9-CM data and 58,805 patients in the ICD-10 data, frequencies of the 17 Charlson comorbidities and the 30 Elixhauser comorbidities remained generally similar across algorithms. The new ICD-10 and enhanced ICD-9-CM coding algorithms either matched or outperformed the original Deyo and Elixhauser ICD-9-CM coding algorithms in predicting in-hospital mortality. The C-statistic was 0.842 for Deyo's ICD-9-CM coding algorithm, 0.860 for the ICD-10 coding algorithm, and 0.859 for the enhanced ICD-9-CM coding algorithm, 0.868 for the original Elixhauser ICD-9-CM coding algorithm, 0.870 for the ICD-10 coding algorithm and 0.878 for the enhanced ICD-9-CM coding algorithm. These newly developed ICD-10 and ICD-9-CM comorbidity coding algorithms produce similar estimates of comorbidity prevalence in administrative data, and may outperform existing ICD-9-CM coding algorithms.
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            Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies

            The propensity score is defined as a subject's probability of treatment selection, conditional on observed baseline covariates. Weighting subjects by the inverse probability of treatment received creates a synthetic sample in which treatment assignment is independent of measured baseline covariates. Inverse probability of treatment weighting (IPTW) using the propensity score allows one to obtain unbiased estimates of average treatment effects. However, these estimates are only valid if there are no residual systematic differences in observed baseline characteristics between treated and control subjects in the sample weighted by the estimated inverse probability of treatment. We report on a systematic literature review, in which we found that the use of IPTW has increased rapidly in recent years, but that in the most recent year, a majority of studies did not formally examine whether weighting balanced measured covariates between treatment groups. We then proceed to describe a suite of quantitative and qualitative methods that allow one to assess whether measured baseline covariates are balanced between treatment groups in the weighted sample. The quantitative methods use the weighted standardized difference to compare means, prevalences, higher‐order moments, and interactions. The qualitative methods employ graphical methods to compare the distribution of continuous baseline covariates between treated and control subjects in the weighted sample. Finally, we illustrate the application of these methods in an empirical case study. We propose a formal set of balance diagnostics that contribute towards an evolving concept of ‘best practice’ when using IPTW to estimate causal treatment effects using observational data. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
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              Revisiting the Behavioral Model and Access to Medical Care: Does it Matter?

              The Behavioral Model of Health Services Use was initially developed over 25 years ago. In the interim it has been subject to considerable application, reprobation, and alteration. I review its development and assess its continued relevance.
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                Author and article information

                Journal
                Patient Prefer Adherence
                Patient Prefer Adherence
                ppa
                ppa
                Patient preference and adherence
                Dove
                1177-889X
                04 November 2020
                2020
                : 14
                : 2187-2199
                Affiliations
                [1 ]Pharmaceutical Health Outcomes and Policy, College of Pharmacy, University of Houston , Houston, TX, USA
                [2 ]Department of Neurology, Baylor College of Medicine , Houston, TX, USA
                Author notes
                Correspondence: Rajender R AparasuPharmaceutical Health Outcomes and Policy, College of Pharmacy, University of Houston, Texas Medical Center , 4849 Calhoun Road, Houston, TX77204-5047, USATel +1 832-842-8374 Email rraparasu@uh.edu
                Author information
                http://orcid.org/0000-0002-6688-5273
                http://orcid.org/0000-0001-6017-7500
                http://orcid.org/0000-0003-2310-901X
                Article
                270557
                10.2147/PPA.S270557
                7649232
                33177813
                3a9ab830-17d0-440a-8922-cc60846b79e0
                © 2020 Earla 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. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms ( https://www.dovepress.com/terms.php).

                History
                : 28 July 2020
                : 10 October 2020
                Page count
                Figures: 2, Tables: 8, References: 52, Pages: 13
                Funding
                Funded by: funded/sponsored study;
                This is not a funded/sponsored study.
                Categories
                Original Research

                Medicine
                group based trajectory modelling,gbtm,adherence trajectory,multiple sclerosis,disease modifying agent,dma,fingolimod,injectable dma

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