Trajectories of Follow-up Compliance in a Fracture Liaison Service and Their Predictors: A Longitudinal Group-Based Trajectory Analysis

Introduction/Objectives Identification of groups of patients following similar trajectories of time-varying patient characteristics are often of considerable clinical value. This study provides an example of how the identification of trajectory groups of patients can be useful. Methods Using clinica...

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Autores principales:	Andréa Senay, Julio C Fernandes, Josée Delisle, Suzanne N Morin, Daniel Nagin, Sylvie Perreault
Formato:	article
Lenguaje:	EN
Publicado:	SAGE Publishing 2021
Materias:	Medicine (General) R5-920 Public aspects of medicine RA1-1270
Acceso en línea:	https://doaj.org/article/6845c522d6bc474d81cbe2af688f6649
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spelling	oai:doaj.org-article:6845c522d6bc474d81cbe2af688f66492021-11-12T09:03:19ZTrajectories of Follow-up Compliance in a Fracture Liaison Service and Their Predictors: A Longitudinal Group-Based Trajectory Analysis2333-392810.1177/23333928211047024https://doaj.org/article/6845c522d6bc474d81cbe2af688f66492021-10-01T00:00:00Zhttps://doi.org/10.1177/23333928211047024https://doaj.org/toc/2333-3928Introduction/Objectives Identification of groups of patients following similar trajectories of time-varying patient characteristics are often of considerable clinical value. This study provides an example of how the identification of trajectory groups of patients can be useful. Methods Using clinical and administrative data of a prospective cohort study aiming to improve the secondary prevention of osteoporosis-related fractures with a Fracture Liaison Service (FLS), trajectory groups for visit compliance over time (2-year follow-up) were predicted using group-based trajectory modeling. Predictors of trajectory groups were identified using multinomial logistic regressions. Results Among 532 participants (86% women, mean age 63 years), three trajectories were identified and interpreted as high followers, intermediate followers, and low followers. The predicted probability for group-membership was: 48.4% high followers, 28.1% intermediate followers, 23.5% low followers. A lower femoral bone mineral density and polypharmacy were predictors of being in the high followers compared to the low followers group; predictors for being in the intermediate followers group were polypharmacy and referral to a bone specialist at baseline. Conclusions Results provided information on visit compliance patterns and predictors for the patients undergoing the intervention. This information has important implications when implementing such health services and determining their effectiveness.Andréa SenayJulio C FernandesJosée DelisleSuzanne N MorinDaniel NaginSylvie PerreaultSAGE PublishingarticleMedicine (General)R5-920Public aspects of medicineRA1-1270ENHealth Services Research & Managerial Epidemiology, Vol 8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine (General) R5-920 Public aspects of medicine RA1-1270
spellingShingle	Medicine (General) R5-920 Public aspects of medicine RA1-1270 Andréa Senay Julio C Fernandes Josée Delisle Suzanne N Morin Daniel Nagin Sylvie Perreault Trajectories of Follow-up Compliance in a Fracture Liaison Service and Their Predictors: A Longitudinal Group-Based Trajectory Analysis
description	Introduction/Objectives Identification of groups of patients following similar trajectories of time-varying patient characteristics are often of considerable clinical value. This study provides an example of how the identification of trajectory groups of patients can be useful. Methods Using clinical and administrative data of a prospective cohort study aiming to improve the secondary prevention of osteoporosis-related fractures with a Fracture Liaison Service (FLS), trajectory groups for visit compliance over time (2-year follow-up) were predicted using group-based trajectory modeling. Predictors of trajectory groups were identified using multinomial logistic regressions. Results Among 532 participants (86% women, mean age 63 years), three trajectories were identified and interpreted as high followers, intermediate followers, and low followers. The predicted probability for group-membership was: 48.4% high followers, 28.1% intermediate followers, 23.5% low followers. A lower femoral bone mineral density and polypharmacy were predictors of being in the high followers compared to the low followers group; predictors for being in the intermediate followers group were polypharmacy and referral to a bone specialist at baseline. Conclusions Results provided information on visit compliance patterns and predictors for the patients undergoing the intervention. This information has important implications when implementing such health services and determining their effectiveness.
format	article
author	Andréa Senay Julio C Fernandes Josée Delisle Suzanne N Morin Daniel Nagin Sylvie Perreault
author_facet	Andréa Senay Julio C Fernandes Josée Delisle Suzanne N Morin Daniel Nagin Sylvie Perreault
author_sort	Andréa Senay
title	Trajectories of Follow-up Compliance in a Fracture Liaison Service and Their Predictors: A Longitudinal Group-Based Trajectory Analysis
title_short	Trajectories of Follow-up Compliance in a Fracture Liaison Service and Their Predictors: A Longitudinal Group-Based Trajectory Analysis
title_full	Trajectories of Follow-up Compliance in a Fracture Liaison Service and Their Predictors: A Longitudinal Group-Based Trajectory Analysis
title_fullStr	Trajectories of Follow-up Compliance in a Fracture Liaison Service and Their Predictors: A Longitudinal Group-Based Trajectory Analysis
title_full_unstemmed	Trajectories of Follow-up Compliance in a Fracture Liaison Service and Their Predictors: A Longitudinal Group-Based Trajectory Analysis
title_sort	trajectories of follow-up compliance in a fracture liaison service and their predictors: a longitudinal group-based trajectory analysis
publisher	SAGE Publishing
publishDate	2021
url	https://doaj.org/article/6845c522d6bc474d81cbe2af688f6649
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Trajectories of Follow-up Compliance in a Fracture Liaison Service and Their Predictors: A Longitudinal Group-Based Trajectory Analysis

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