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Using group based trajectory modeling for assessing medication adherence to nintedanib among idiopathic pulmonary fibrosis patients

BACKGROUND AND OBJECTIVE: Adherence to antifibrotic medications has been evaluated in a few studies using annual proportion of days covered (PDC), a common adherence metric. However, PDC alone cannot identify and distinguish between different patterns of adherence over time, which can be accomplishe...

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Autores principales: Nili, Mona, Epstein, Andrew J., Nunag, Dominic, Olson, Amy, Borah, Bijan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10303848/
https://www.ncbi.nlm.nih.gov/pubmed/37370093
http://dx.doi.org/10.1186/s12890-023-02496-3
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author Nili, Mona
Epstein, Andrew J.
Nunag, Dominic
Olson, Amy
Borah, Bijan
author_facet Nili, Mona
Epstein, Andrew J.
Nunag, Dominic
Olson, Amy
Borah, Bijan
author_sort Nili, Mona
collection PubMed
description BACKGROUND AND OBJECTIVE: Adherence to antifibrotic medications has been evaluated in a few studies using annual proportion of days covered (PDC), a common adherence metric. However, PDC alone cannot identify and distinguish between different patterns of adherence over time, which can be accomplished using group-based trajectory models (GBTM) of monthly PDC. The objective is to assess nintedanib adherence trajectories using GBTM and identify characteristics of patients within each trajectory group. METHODS: Individuals with idiopathic pulmonary fibrosis (IPF) who initiated nintedanib during 10/1/2014–12/31/2018 were identified in 100% Medicare claims and enrollment data. The sample consisted of community-dwelling older adults (≥ 66 years) with continuous coverage in Medicare Parts A, B and D for one year before (baseline) and after (follow-up) initiating nintedanib. A series of GBTMs of adherence was estimated to identify the best-fitting specification. Patients were then grouped based on their estimated adherence trajectories. Associations between baseline patient characteristics, including demographics, comorbidities, and health care use, and group membership probabilities were quantified as odds ratios using fractional multinomial logit modeling. RESULTS: Among the 1,798 patients initiating nintedanib, mean age was 75.4 years, 61.1% were male, and 91.1% were non-Hispanic white. The best-fitting GBTM had five adherence trajectory groups: high adherence (43.1%), moderate adherence (11.9%), high-then-poor adherence (10.4%), delayed-poor adherence (13.2%), and early-poor adherence (21.5%). The principal factors associated with higher odds of being in at least one of the poor-adherence groups were older age, female sex, race and ethnicity other than non-Hispanic white, and number of medications during baseline. CONCLUSIONS: GBTM identified distinct patterns of nintedanib adherence for the IPF patient cohort. Identifying adherence trajectory groups and understanding the characteristics of their members provide more actionable information to personalize interventions than conventional metrics of medication adherence. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12890-023-02496-3.
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spelling pubmed-103038482023-06-29 Using group based trajectory modeling for assessing medication adherence to nintedanib among idiopathic pulmonary fibrosis patients Nili, Mona Epstein, Andrew J. Nunag, Dominic Olson, Amy Borah, Bijan BMC Pulm Med Research BACKGROUND AND OBJECTIVE: Adherence to antifibrotic medications has been evaluated in a few studies using annual proportion of days covered (PDC), a common adherence metric. However, PDC alone cannot identify and distinguish between different patterns of adherence over time, which can be accomplished using group-based trajectory models (GBTM) of monthly PDC. The objective is to assess nintedanib adherence trajectories using GBTM and identify characteristics of patients within each trajectory group. METHODS: Individuals with idiopathic pulmonary fibrosis (IPF) who initiated nintedanib during 10/1/2014–12/31/2018 were identified in 100% Medicare claims and enrollment data. The sample consisted of community-dwelling older adults (≥ 66 years) with continuous coverage in Medicare Parts A, B and D for one year before (baseline) and after (follow-up) initiating nintedanib. A series of GBTMs of adherence was estimated to identify the best-fitting specification. Patients were then grouped based on their estimated adherence trajectories. Associations between baseline patient characteristics, including demographics, comorbidities, and health care use, and group membership probabilities were quantified as odds ratios using fractional multinomial logit modeling. RESULTS: Among the 1,798 patients initiating nintedanib, mean age was 75.4 years, 61.1% were male, and 91.1% were non-Hispanic white. The best-fitting GBTM had five adherence trajectory groups: high adherence (43.1%), moderate adherence (11.9%), high-then-poor adherence (10.4%), delayed-poor adherence (13.2%), and early-poor adherence (21.5%). The principal factors associated with higher odds of being in at least one of the poor-adherence groups were older age, female sex, race and ethnicity other than non-Hispanic white, and number of medications during baseline. CONCLUSIONS: GBTM identified distinct patterns of nintedanib adherence for the IPF patient cohort. Identifying adherence trajectory groups and understanding the characteristics of their members provide more actionable information to personalize interventions than conventional metrics of medication adherence. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12890-023-02496-3. BioMed Central 2023-06-27 /pmc/articles/PMC10303848/ /pubmed/37370093 http://dx.doi.org/10.1186/s12890-023-02496-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Nili, Mona
Epstein, Andrew J.
Nunag, Dominic
Olson, Amy
Borah, Bijan
Using group based trajectory modeling for assessing medication adherence to nintedanib among idiopathic pulmonary fibrosis patients
title Using group based trajectory modeling for assessing medication adherence to nintedanib among idiopathic pulmonary fibrosis patients
title_full Using group based trajectory modeling for assessing medication adherence to nintedanib among idiopathic pulmonary fibrosis patients
title_fullStr Using group based trajectory modeling for assessing medication adherence to nintedanib among idiopathic pulmonary fibrosis patients
title_full_unstemmed Using group based trajectory modeling for assessing medication adherence to nintedanib among idiopathic pulmonary fibrosis patients
title_short Using group based trajectory modeling for assessing medication adherence to nintedanib among idiopathic pulmonary fibrosis patients
title_sort using group based trajectory modeling for assessing medication adherence to nintedanib among idiopathic pulmonary fibrosis patients
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10303848/
https://www.ncbi.nlm.nih.gov/pubmed/37370093
http://dx.doi.org/10.1186/s12890-023-02496-3
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