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      Non-Adherence to Anti-Retroviral Therapy Among Adult People Living with HIV in Ethiopia: Systematic Review and Meta-Analysis

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          Abstract

          Human immunodeficiency virus remains a global public health problem. Despite efforts to determine the prevalence of non-adherence to ART and its predictors in Ethiopia, various primary studies presented inconsistent findings. Therefore, this review aimed to determine the pooled prevalence of non-adherence to ART and identify its predictors. We have searched PubMed, Google Scholar and Web of Science databases extensively for all available studies. A weighted inverse-variance random-effects model was used to compute the overall non-adherence to ART. The pooled prevalence of non-adherence to ART was 20.68% (95% CI: 17.74, 23.61); I 2 = 98.40%; p < 0.001). Educational level of primary school and lower [AOR = 3.5, 95%CI: 1.7, 7.4], taking co-medications [AOR = 0.45, 95%CI: 0.35, 0.59], not using memory aids [AOR = 0.30, 95%CI: 0.13, 0.71], depression [AOR = 2.0, 95%CI: 1.05, 3.79], comorbidity [AOR = 2.12, 95%CI: 1.16, 3.09), under-nutrition [AOR = 2.02, 95%CI: 1.20, 3.43], not believing on ART can control HIV [AOR = 2.31, 95%CI: 1.92, 2.77], lack of access to health facilities [AOR = 3.86, 95%CI: 1.10, 13.51] and taking ART pills uncomfortably while others looking [AOR = 5.21, 95%CI: 2.56, 10.53] were significantly associated with non-adherence to anti-retroviral therapy. The overall pooled prevalence of non-adherence to ART was considerably high in Ethiopia. Educational status, taking co-medications, not using memory aids, depression, comorbidity, under nutrition, not believing on anti-retroviral therapy controls HIV, lack of access to health facilities and taking ART pills uncomfortably were independent predictors of non-adherence to ART in Ethiopia. Therefore, healthcare providers, adherence counselors and supporters should detect non-adherence behaviors and patients’ difficulties with ART early, and provide intensive counseling to promote adherence.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s10461-023-04252-4.

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          Measuring inconsistency in meta-analyses.

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            The PRISMA 2020 statement: An updated guideline for reporting systematic reviews

            The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.
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              A basic introduction to fixed-effect and random-effects models for meta-analysis.

              There are two popular statistical models for meta-analysis, the fixed-effect model and the random-effects model. The fact that these two models employ similar sets of formulas to compute statistics, and sometimes yield similar estimates for the various parameters, may lead people to believe that the models are interchangeable. In fact, though, the models represent fundamentally different assumptions about the data. The selection of the appropriate model is important to ensure that the various statistics are estimated correctly. Additionally, and more fundamentally, the model serves to place the analysis in context. It provides a framework for the goals of the analysis as well as for the interpretation of the statistics. In this paper we explain the key assumptions of each model, and then outline the differences between the models. We conclude with a discussion of factors to consider when choosing between the two models. Copyright © 2010 John Wiley & Sons, Ltd. Copyright © 2010 John Wiley & Sons, Ltd.
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                Author and article information

                Contributors
                tigabumunye21@gmail.com
                Journal
                AIDS Behav
                AIDS Behav
                AIDS and Behavior
                Springer US (New York )
                1090-7165
                1573-3254
                29 December 2023
                29 December 2023
                2024
                : 28
                : 2
                : 609-624
                Affiliations
                [1 ]Department of Nursing, College of Health Sciences, Debre Tabor University, ( https://ror.org/02bzfxf13) Debre Tabor, Ethiopia
                [2 ]Department of Maternity and Neonatal Nursing, College of Health Sciences, Debre Tabor University, ( https://ror.org/02bzfxf13) Debre Tabor, Ethiopia
                [3 ]School of Public Health, University of Technology Sydney, ( https://ror.org/03f0f6041) Sydney, NSW Australia
                [4 ]Department of Reproductive Health, College of Health Sciences, Debre Tabor University, ( https://ror.org/02bzfxf13) Debre Tabor, Ethiopia
                [5 ]Department of Pediatrics and Child health Nursing, College of Health Sciences, Debre Tabor University, ( https://ror.org/02bzfxf13) Debre Tabor, Ethiopia
                [6 ]Department of Psychiatry, College of Health Sciences, Debre Tabor University, ( https://ror.org/02bzfxf13) Debre Tabor, Ethiopia
                [7 ]Department of Emergency and Critical Care Nursing, College of Health Sciences, Debre Tabor University, ( https://ror.org/02bzfxf13) Debre Tabor, Ethiopia
                [8 ]Department of Medical Laboratory, College of Health Sciences, Debre Tabor University, ( https://ror.org/02bzfxf13) Debre Tabor, Ethiopia
                Article
                4252
                10.1007/s10461-023-04252-4
                10876791
                38157133
                0efd92c0-0ba4-46d9-a8d0-d6b54d8cc61d
                © The Author(s) 2023

                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/.

                History
                : 8 December 2023
                Categories
                Original Paper
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                © Springer Science+Business Media, LLC, part of Springer Nature 2024

                Infectious disease & Microbiology
                non-adherence to art,predictors,ethiopia, meta-analysis
                Infectious disease & Microbiology
                non-adherence to art, predictors, ethiopia, meta-analysis

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