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      Factors Associated With Nonadherence to Antiretroviral Therapy Among Young People Living With Perinatally Acquired HIV in England

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          Abstract

          Young people living with perinatally acquired HIV may be at risk of poor adherence to antiretroviral therapy; identification of predictors, using a conceptual framework approach proposed previously by others, is important to identify those at higher risk. In 261 young people with perinatally acquired HIV in England, 70 (27%) reported 3-day nonadherence, 82 (31%) last month nonadherence, and 106 (41%) nonadherence on either measure. Of those reporting nonadherence on both measures, 52% (23/44) had viral load of <50 copies/ml, compared with 88% (127/145) of those reported being fully adherent. In multivariable analysis, young person and medication theme factors were associated with nonadherence. The main predictors of 3-day nonadherence were antiretroviral therapy containing a boosted protease inhibitor and poorer quality of life. Predictors of last month nonadherence were having told more people about one's HIV status, worse self-perception about having HIV, and boosted protease inhibitor–based regimens. The consistency of individual young person and medication factors in predicting nonadherence gives insight into where interventions may best be targeted to improve adherence.

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            Our future: a Lancet commission on adolescent health and wellbeing

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              Purposeful selection of variables in logistic regression

              Background The main problem in many model-building situations is to choose from a large set of covariates those that should be included in the "best" model. A decision to keep a variable in the model might be based on the clinical or statistical significance. There are several variable selection algorithms in existence. Those methods are mechanical and as such carry some limitations. Hosmer and Lemeshow describe a purposeful selection of covariates within which an analyst makes a variable selection decision at each step of the modeling process. Methods In this paper we introduce an algorithm which automates that process. We conduct a simulation study to compare the performance of this algorithm with three well documented variable selection procedures in SAS PROC LOGISTIC: FORWARD, BACKWARD, and STEPWISE. Results We show that the advantage of this approach is when the analyst is interested in risk factor modeling and not just prediction. In addition to significant covariates, this variable selection procedure has the capability of retaining important confounding variables, resulting potentially in a slightly richer model. Application of the macro is further illustrated with the Hosmer and Lemeshow Worchester Heart Attack Study (WHAS) data. Conclusion If an analyst is in need of an algorithm that will help guide the retention of significant covariates as well as confounding ones they should consider this macro as an alternative tool.
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                Author and article information

                Journal
                J Assoc Nurses AIDS Care
                J Assoc Nurses AIDS Care
                JANAC
                JANAC
                JNC
                The Journal of the Association of Nurses in AIDS Care
                Wolters Kluwer (Philadelphia, PA )
                1055-3290
                1552-6917
                Sep-Oct 2020
                27 May 2020
                : 31
                : 5
                : 574-586
                Affiliations
                Ali Judd, MSc, PhD, is a Professor of Epidemiology, MRC Clinical Trials Unit at UCL, University College London, London, United Kingdom. Diane Melvin, CPsychol, AFBPsS, is a Clinical Psychologist, Imperial College Healthcare National Health Service (NHS) Trust, London, United Kingdom. Lindsay C. Thompson, MPhil, MSc, is a Statistician, MRC Clinical Trials Unit at UCL, University College London, London, United Kingdom. Caroline Foster, MBBS, MRCPCH, is a Consultant, Adolescent HIV and Infectious Diseases, Imperial College Healthcare NHS Trust, London, United Kingdom. Marthe Le Prevost, MSc, is a Senior Research Nurse, MRC Clinical Trials Unit at UCL, University College London, London, United Kingdom. Michael Evangeli, MA, MSc, ClinPsyD, is a Reader, Clinical Psychology, Royal Holloway, University of London, London, United Kingdom. Alan Winston, MBChB, MRCP, MD, is Professor of Genito-Urinary Medicine, Imperial College London, London, United Kingdom. Alejandro Arenas-Pinto, MBBS, MSc, PhD, is a Clinical Principal Research Fellow, MRC Clinical Trials Unit at UCL, University College London, London, United Kingdom. Kate Sturgeon, MSc, is a Senior Research Nurse, MRC Clinical Trials Unit at UCL, University College London, London, United Kingdom. Katie Rowson, MSc, is a Research Nurse, MRC Clinical Trials Unit at UCL, University College London, London, United Kingdom. Diana M. Gibb, MD, MRCP, MSc, is Professor of Epidemiology, MRC Clinical Trials Unit at UCL, University College London, London, United Kingdom. Hannah Castro, MSc, is a Senior Statistician, MRC Clinical Trials Unit at UCL, University College London, London, United Kingdom.
                Author notes
                [* ]Corresponding author: Ali Judd, e-mail: a.judd@ 123456ucl.ac.uk
                Article
                JANAC-D-19-00195 00010
                10.1097/JNC.0000000000000171
                7497417
                32467489
                510b80ea-86e9-420a-a6c5-c3289316141f
                Copyright © 2020 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Association of Nurses in AIDS Care.

                This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

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                Research Article
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                adherence,adolescents and young adults,conceptual framework,hiv,perinatal,young people

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